Merge pull request #3877 from pipecat-ai/pk/service-init-cleanup

Add `settings` as canonical init arg for all AIService descendants, d…

COMMUNITY_INTEGRATIONS.md

@@ -231,49 +231,102 @@ def can_generate_metrics(self) -> bool:
     return True
 ```
 
-### Dynamic Settings Updates
+### Service Settings
 
-STT, LLM, and TTS services support runtime configuration changes via `*UpdateSettingsFrame`s (e.g. `STTUpdateSettingsFrame`, `TTSUpdateSettingsFrame`, `LLMUpdateSettingsFrame`).
+Every STT, LLM, TTS, and image-generation service exposes a **Settings dataclass** that serves two roles:
 
-Each service declares a settings dataclass that extends the appropriate base (`STTSettings`, `TTSSettings`, `LLMSettings`). Fields default to `NOT_GIVEN` so that update objects can represent sparse deltas:
+1. **Store mode** — the service's `self._settings` holds the current value of every runtime-updatable field.
+2. **Delta mode** — an update frame carries only the fields that changed; unset fields remain `NOT_GIVEN`.
+
+#### Defining your Settings class
+
+Extend `STTSettings`, `TTSSettings`, `LLMSettings`, or `ImageGenSettings`. The base classes already provide common fields (e.g. `model`, `voice`, `language`). You only need to add **service-specific knobs that should be runtime-updatable**:
 
 ```python
 from dataclasses import dataclass, field
 
-from pipecat.services.settings import STTSettings, NOT_GIVEN
+from pipecat.services.settings import TTSSettings, NOT_GIVEN
 
 @dataclass
-class MySTTSettings(STTSettings):
+class MyTTSSettings(TTSSettings):
-    """Settings for my STT service.
+    """Settings for MyTTS service.
 
     Parameters:
-        region: Cloud region for the service.
+        speaking_rate: Speed multiplier (0.5–2.0).
     """
 
-    region: str = field(default_factory=lambda: NOT_GIVEN)
+    speaking_rate: float | None = field(default_factory=lambda: NOT_GIVEN)
 ```
 
-The service stores its current settings in `self._settings` and declares the type with a class-level annotation for editor support:
+**What goes in Settings vs. `__init__` params:**
+
+| Belongs in Settings                                      | Stays as `__init__` params                |
+| -------------------------------------------------------- | ----------------------------------------- |
+| Model name, voice, language                              | API keys, auth tokens                     |
+| Service-specific tuning knobs (rate, pitch, temperature) | Base URLs, endpoint overrides             |
+| Anything users may want to change mid-session            | Audio encoding, sample format             |
+|                                                          | Connection parameters (timeouts, retries) |
+
+The rule of thumb: if a caller might send an update frame to change it at runtime, it belongs in Settings. Everything else is init-only config stored as `self._xxx`.
+
+#### Wiring settings into `__init__`
+
+Accept an **optional** `settings` parameter. Build a `default_settings` object with all fields set to real values, then merge any caller overrides with `apply_update`:
 
 ```python
-class MySTTService(STTService):
+from typing import Optional
-    _settings: MySTTSettings
 
-    def __init__(self, *, model: str, language: str, region: str, **kwargs):
+class MyTTSService(TTSService):
-        # An initial value should be provided for every settings field.
+    _settings: MyTTSSettings
-        # This will be validated at service start.
+
-        # (If you track sample_rate, it can be a placeholder value like 0; see
+    def __init__(
-        # "Sample Rate Handling").
+        self,
-        super().__init__(
+        *,
-            settings=MySTTSettings(model=model, language=language, region=region), **kwargs
+        api_key: str,
+        settings: Optional[MyTTSSettings] = None,
+        **kwargs,
+    ):
+        # 1. Defaults — every field has a real value (store mode).
+        default_settings = MyTTSSettings(
+            model="my-model-v1",
+            voice="default-voice",
+            language="en",
+            speaking_rate=1.0,
         )
+
+        # 2. Merge caller overrides (only given fields win).
+        if settings is not None:
+            default_settings.apply_update(settings)
+
+        # 3. Pass the fully-populated settings to the base class.
+        super().__init__(settings=default_settings, **kwargs)
+
+        # 4. Init-only config stored separately.
+        self._api_key = api_key
 ```
 
+This pattern lets callers override only what they care about:
+
+```python
+# Uses all defaults
+svc = MyTTSService(api_key="sk-xxx")
+
+# Overrides just the voice
+svc = MyTTSService(
+    api_key="sk-xxx",
+    settings=MyTTSSettings(voice="custom-voice"),
+)
+```
+
+#### Reacting to runtime changes
+
+STT, LLM, and TTS services support runtime configuration changes via `*UpdateSettingsFrame`s (e.g. `STTUpdateSettingsFrame`, `TTSUpdateSettingsFrame`, `LLMUpdateSettingsFrame`).
+
 To react to runtime setting changes, override `_update_settings`. The base implementation applies the delta to `self._settings` and returns a `dict` mapping each changed field name to its **pre-update** value. Your override should call `super()` first, then act on the changed fields. A common implementation might look like:
 
 ```python
-async def _update_settings(self, update: STTSettings) -> dict[str, Any]:
+async def _update_settings(self, update: TTSSettings) -> dict[str, Any]:
-    """Apply a settings update, reconfiguring the recognizer if needed."""
+    """Apply a settings update, reconfiguring the connection if needed."""
     changed = await super()._update_settings(update)
 
     if not changed:
@@ -292,7 +345,7 @@ Note that, in this example, the service requires a reconnect to apply the new la
 If your service can't yet apply certain settings at runtime, call `self._warn_unhandled_updated_settings(changed)` with any unhandled field names so users get a clear log message:
 
 ```python
-async def _update_settings(self, update: STTSettings) -> dict[str, Any]:
+async def _update_settings(self, update: TTSSettings) -> dict[str, Any]:
     changed = await super()._update_settings(update)
 
     if not changed:

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

@@ -39,7 +39,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
         tts = PiperHttpTTSService(
-            base_url=os.getenv("PIPER_BASE_URL"), aiohttp_session=session, sample_rate=24000
+            base_url=os.getenv("PIPER_BASE_URL"),
+            aiohttp_session=session,
+            sample_rate=24000,
         )
 
         task = PipelineTask(

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

@@ -16,7 +16,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.rime.tts import RimeHttpTTSService
+from pipecat.services.rime.tts import RimeHttpTTSService, RimeTTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -39,8 +39,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async with aiohttp.ClientSession() as session:
         tts = RimeHttpTTSService(
             api_key=os.getenv("RIME_API_KEY", ""),
-            voice_id="rex",
             aiohttp_session=session,
+            settings=RimeTTSSettings(
+                voice="rex",
+            ),
         )
 
         task = PipelineTask(

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

@@ -15,7 +15,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -37,7 +37,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     task = PipelineTask(

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

@@ -15,7 +15,7 @@ from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
 
 load_dotenv(override=True)
@@ -29,7 +29,9 @@ async def main():
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     pipeline = Pipeline([tts, transport.output()])

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

@@ -16,7 +16,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.runner.livekit import configure
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.transports.livekit.transport import LiveKitParams, LiveKitTransport
 
 load_dotenv(override=True)
@@ -37,7 +37,9 @@ async def main():
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     runner = PipelineRunner()

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

@@ -16,8 +16,8 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -39,12 +39,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are an LLM in a WebRTC session, and this is a 'hello world' demo.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are an LLM in a WebRTC session, and this is a 'hello world' demo.",
+        ),
     )
 
     task = PipelineTask(

examples/foundational/03-still-frame.py

@@ -16,7 +16,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.fal.image import FalImageGenService
+from pipecat.services.fal.image import FalImageGenService, FalImageGenSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
@@ -45,7 +45,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
         imagegen = FalImageGenService(
-            params=FalImageGenService.InputParams(image_size="square_hd"),
+            settings=FalImageGenSettings(
+                image_size="square_hd",
+            ),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )

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

@@ -17,7 +17,7 @@ from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.fal.image import FalImageGenService
+from pipecat.services.fal.image import FalImageGenService, FalImageGenSettings
 from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
 
 load_dotenv(override=True)
@@ -37,7 +37,9 @@ async def main():
         )
 
         imagegen = FalImageGenService(
-            params=FalImageGenService.InputParams(image_size="square_hd"),
+            settings=FalImageGenSettings(
+                image_size="square_hd",
+            ),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )

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

@@ -27,9 +27,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
     LLMUserAggregatorParams,
 )
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.smallwebrtc.connection import IceServer, SmallWebRTCConnection
 from pipecat.transports.smallwebrtc.transport import SmallWebRTCTransport
@@ -67,12 +67,16 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMUserAggregatorParams,
 )
 from pipecat.runner.daily import configure
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.daily.transport import DailyParams, DailyTransport
 
 load_dotenv(override=True)
@@ -50,13 +50,17 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            settings=CartesiaTTSSettings(
+                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            ),
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o",
+            settings=OpenAILLMSettings(
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+                model="gpt-4o",
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

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

@@ -29,9 +29,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMUserAggregatorParams,
 )
 from pipecat.runner.livekit import configure
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.livekit.transport import LiveKitParams, LiveKitTransport
 
 load_dotenv(override=True)
@@ -57,12 +57,16 @@ async def main():
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     context = LLMContext()

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

@@ -27,8 +27,8 @@ from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaHttpTTSService
+from pipecat.services.cartesia.tts import CartesiaHttpTTSService, CartesiaTTSSettings
-from pipecat.services.fal.image import FalImageGenService
+from pipecat.services.fal.image import FalImageGenService, FalImageGenSettings
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -98,11 +98,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            settings=CartesiaTTSSettings(
+                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            ),
         )
 
         imagegen = FalImageGenService(
-            params=FalImageGenService.InputParams(image_size="square_hd"),
+            settings=FalImageGenSettings(
+                image_size="square_hd",
+            ),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )

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

@@ -28,8 +28,8 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.cartesia.tts import CartesiaHttpTTSService
+from pipecat.services.cartesia.tts import CartesiaHttpTTSService, CartesiaTTSSettings
-from pipecat.services.fal.image import FalImageGenService
+from pipecat.services.fal.image import FalImageGenService, FalImageGenSettings
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
 
@@ -98,11 +98,15 @@ async def main():
 
             tts = CartesiaHttpTTSService(
                 api_key=os.getenv("CARTESIA_API_KEY"),
-                voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+                settings=CartesiaTTSSettings(
+                    voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+                ),
             )
 
             imagegen = FalImageGenService(
-                params=FalImageGenService.InputParams(image_size="square_hd"),
+                settings=FalImageGenSettings(
+                    image_size="square_hd",
+                ),
                 aiohttp_session=session,
                 key=os.getenv("FAL_KEY"),
             )

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

@@ -28,9 +28,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -83,12 +83,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     ml = MetricsLogger()

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

@@ -29,9 +29,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
@@ -100,12 +100,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.stt import CartesiaSTTService
-from pipecat.services.cartesia.tts import CartesiaHttpTTSService
+from pipecat.services.cartesia.tts import CartesiaHttpTTSService, CartesiaTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -58,13 +58,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
             aiohttp_session=session,
+            settings=CartesiaTTSSettings(
+                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            ),
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

examples/foundational/07-interruptible.py

@@ -21,9 +21,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.services.tts_service import TextAggregationMode
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -56,15 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
-        # Alternatively, you can use TextAggregationMode.TOKEN to stream tokens instead of
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        # sentencesfor faster response times.
+        ),
-        # text_aggregation_mode=TextAggregationMode.TOKEN,
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -21,10 +21,10 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.openai.base_llm import BaseOpenAILLMService
+from pipecat.services.openai.base_llm import OpenAILLMSettings
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
+from pipecat.services.speechmatics.stt import SpeechmaticsSTTService, SpeechmaticsSTTSettings
-from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
+from pipecat.services.speechmatics.tts import SpeechmaticsTTSService, SpeechmaticsTTSSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -93,7 +93,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async with aiohttp.ClientSession() as session:
         stt = SpeechmaticsSTTService(
             api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            params=SpeechmaticsSTTService.InputParams(
+            settings=SpeechmaticsSTTSettings(
                 language=Language.EN,
                 turn_detection_mode=SpeechmaticsSTTService.TurnDetectionMode.ADAPTIVE,
                 # focus_speakers=["S1"],
@@ -104,32 +104,21 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = SpeechmaticsTTSService(
             api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            voice_id="sarah",
+            settings=SpeechmaticsTTSSettings(
+                voice="sarah",
+            ),
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            params=BaseOpenAILLMService.InputParams(temperature=0.75),
+            settings=OpenAILLMSettings(
+                temperature=0.75,
+                system_instruction="You are a helpful British assistant called Sarah. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Always include punctuation in your responses. Give very short replies - do not give longer replies unless strictly necessary. Respond to what the user said in a concise, funny, creative and helpful way. Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to.",
+            ),
         )
 
-        messages = [
+        context = LLMContext()
-            {
-                "role": "system",
-                "content": (
-                    "You are a helpful British assistant called Sarah. "
-                    "Your goal is to demonstrate your capabilities in a succinct way. "
-                    "Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
-                    "Always include punctuation in your responses. "
-                    "Give very short replies - do not give longer replies unless strictly necessary. "
-                    "Respond to what the user said in a concise, funny, creative and helpful way. "
-                    "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
-                    "Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
-                ),
-            },
-        ]
-
-        context = LLMContext(messages)
         user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
             context,
             user_params=LLMUserAggregatorParams(user_turn_strategies=ExternalUserTurnStrategies()),
@@ -160,7 +149,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         async def on_client_connected(transport, client):
             logger.info(f"Client connected")
             # Kick off the conversation.
-            messages.append({"role": "system", "content": "Say a short hello to the user."})
+            context.add_message({"role": "system", "content": "Say a short hello to the user."})
             await task.queue_frames([LLMRunFrame()])
 
         @transport.event_handler("on_client_disconnected")

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

@@ -22,10 +22,10 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.openai.base_llm import BaseOpenAILLMService
+from pipecat.services.openai.base_llm import OpenAILLMSettings
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
+from pipecat.services.speechmatics.stt import SpeechmaticsSTTService, SpeechmaticsSTTSettings
-from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
+from pipecat.services.speechmatics.tts import SpeechmaticsTTSService, SpeechmaticsTTSSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -76,7 +76,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async with aiohttp.ClientSession() as session:
         stt = SpeechmaticsSTTService(
             api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            params=SpeechmaticsSTTService.InputParams(
+            settings=SpeechmaticsSTTSettings(
                 language=Language.EN,
                 speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
             ),
@@ -84,31 +84,21 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = SpeechmaticsTTSService(
             api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            voice_id="sarah",
+            settings=SpeechmaticsTTSSettings(
+                voice="sarah",
+            ),
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            params=BaseOpenAILLMService.InputParams(temperature=0.75),
+            settings=OpenAILLMSettings(
+                temperature=0.75,
+                system_instruction="You are a helpful British assistant called Sarah. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Always include punctuation in your responses. Give very short replies - do not give longer replies unless strictly necessary. Respond to what the user said in a concise, funny, creative and helpful way. Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to.",
+            ),
         )
 
-        messages = [
+        context = LLMContext()
-            {
-                "role": "system",
-                "content": (
-                    "You are a helpful British assistant called Sarah. "
-                    "Your goal is to demonstrate your capabilities in a succinct way. "
-                    "Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. "
-                    "Always include punctuation in your responses. "
-                    "Give very short replies - do not give longer replies unless strictly necessary. "
-                    "Respond to what the user said in a concise, funny, creative and helpful way. "
-                    "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
-                ),
-            },
-        ]
-
-        context = LLMContext(messages)
         user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
             context,
             user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
@@ -139,7 +129,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         async def on_client_connected(transport, client):
             logger.info(f"Client connected")
             # Kick off the conversation.
-            messages.append({"role": "system", "content": "Say a short hello to the user."})
+            context.add_message({"role": "system", "content": "Say a short hello to the user."})
             await task.queue_frames([LLMRunFrame()])
 
         @transport.event_handler("on_client_disconnected")

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

@@ -28,7 +28,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.processors.frameworks.langchain import LangchainProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -71,7 +71,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     prompt = ChatPromptTemplate.from_messages(

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

@@ -22,9 +22,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.flux.stt import DeepgramFluxSTTService
+from pipecat.services.deepgram.flux.stt import DeepgramFluxSTTService, DeepgramFluxSTTSettings
-from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.deepgram.tts import DeepgramTTSService, DeepgramTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -56,14 +56,23 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramFluxSTTService(
         api_key=os.getenv("DEEPGRAM_API_KEY"),
-        params=DeepgramFluxSTTService.InputParams(min_confidence=0.3),
+        settings=DeepgramFluxSTTSettings(
+            min_confidence=0.3,
+        ),
     )
 
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
+    tts = DeepgramTTSService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
+        settings=DeepgramTTSSettings(
+            voice="aura-2-andromeda-en",
+        ),
+    )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.deepgram.tts import DeepgramHttpTTSService
+from pipecat.services.deepgram.tts import DeepgramHttpTTSService, DeepgramTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -59,18 +59,20 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = DeepgramHttpTTSService(
             api_key=os.getenv("DEEPGRAM_API_KEY"),
-            voice="aura-2-andromeda-en",
+            settings=DeepgramTTSSettings(
+                voice="aura-2-andromeda-en",
+            ),
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
-        messages = []
+        context = LLMContext()
-
-        context = LLMContext(messages)
         user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
             context,
             user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),

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

@@ -22,9 +22,12 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.aws.llm import AWSBedrockLLMService
+from pipecat.services.aws.llm import AWSBedrockLLMService, AWSBedrockLLMSettings
 from pipecat.services.deepgram.sagemaker.stt import DeepgramSageMakerSTTService
-from pipecat.services.deepgram.sagemaker.tts import DeepgramSageMakerTTSService
+from pipecat.services.deepgram.sagemaker.tts import (
+    DeepgramSageMakerTTSService,
+    DeepgramSageMakerTTSSettings,
+)
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -69,14 +72,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     tts = DeepgramSageMakerTTSService(
         endpoint_name=os.getenv("SAGEMAKER_TTS_ENDPOINT_NAME"),
         region=os.getenv("AWS_REGION"),
-        voice="aura-2-andromeda-en",
+        settings=DeepgramSageMakerTTSSettings(
+            voice="aura-2-andromeda-en",
+        ),
     )
 
     llm = AWSBedrockLLMService(
         aws_region=os.getenv("AWS_REGION"),
-        model="us.amazon.nova-pro-v1:0",
+        settings=AWSBedrockLLMSettings(
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+            model="us.amazon.nova-pro-v1:0",
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            temperature=0.8,
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -7,7 +7,6 @@
 
 import os
 
-from deepgram import LiveOptions
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -22,9 +21,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.deepgram.stt import DeepgramSTTService, DeepgramSTTSettings
-from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.deepgram.tts import DeepgramTTSService, DeepgramTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -56,14 +55,24 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(
         api_key=os.getenv("DEEPGRAM_API_KEY"),
-        live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
+        settings=DeepgramSTTSettings(
+            vad_events=True,
+            utterance_end_ms="1000",
+        ),
     )
 
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
+    tts = DeepgramTTSService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
+        settings=DeepgramTTSSettings(
+            voice="aura-2-andromeda-en",
+        ),
+    )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.deepgram.tts import DeepgramTTSService, DeepgramTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -55,11 +55,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
+    tts = DeepgramTTSService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
+        settings=DeepgramTTSSettings(
+            voice="aura-2-andromeda-en",
+        ),
+    )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.elevenlabs.stt import ElevenLabsSTTService
-from pipecat.services.elevenlabs.tts import ElevenLabsHttpTTSService
+from pipecat.services.elevenlabs.tts import ElevenLabsHttpTTSService, ElevenLabsHttpTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -63,13 +63,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = ElevenLabsHttpTTSService(
             api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
             aiohttp_session=session,
+            settings=ElevenLabsHttpTTSSettings(
+                voice=os.getenv("ELEVENLABS_VOICE_ID", ""),
+            ),
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

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

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.elevenlabs.stt import ElevenLabsRealtimeSTTService
-from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
+from pipecat.services.elevenlabs.tts import ElevenLabsTTSService, ElevenLabsTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -57,12 +57,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = ElevenLabsTTSService(
         api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+        settings=ElevenLabsTTSSettings(
+            voice=os.getenv("ELEVENLABS_VOICE_ID", ""),
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,7 +22,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.azure.llm import AzureLLMService
+from pipecat.services.azure.llm import AzureLLMService, AzureLLMSettings
 from pipecat.services.azure.stt import AzureSTTService
 from pipecat.services.azure.tts import AzureHttpTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -66,7 +66,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
         endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
         model=os.getenv("AZURE_CHATGPT_MODEL"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=AzureLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,7 +22,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.azure.llm import AzureLLMService
+from pipecat.services.azure.llm import AzureLLMService, AzureLLMSettings
 from pipecat.services.azure.stt import AzureSTTService
 from pipecat.services.azure.tts import AzureTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -66,7 +66,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
         endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
         model=os.getenv("AZURE_CHATGPT_MODEL"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=AzureLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,9 +22,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-from pipecat.services.openai.stt import OpenAISTTService
+from pipecat.services.openai.stt import OpenAISTTService, OpenAISTTSettings
-from pipecat.services.openai.tts import OpenAITTSService
+from pipecat.services.openai.tts import OpenAITTSService, OpenAITTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -54,15 +54,24 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = OpenAISTTService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o-transcribe",
+        settings=OpenAISTTSettings(
-        prompt="Expect words related to dogs, such as breed names.",
+            model="gpt-4o-transcribe",
+            prompt="Expect words related to dogs, such as breed names.",
+        ),
     )
 
-    tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
+    tts = OpenAITTSService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        settings=OpenAITTSSettings(
+            voice="ballad",
+        ),
+    )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        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.",
+        settings=OpenAILLMSettings(
+            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.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,9 +22,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-from pipecat.services.openai.stt import OpenAIRealtimeSTTService
+from pipecat.services.openai.stt import OpenAIRealtimeSTTService, OpenAIRealtimeSTTSettings
-from pipecat.services.openai.tts import OpenAITTSService
+from pipecat.services.openai.tts import OpenAITTSService, OpenAITTSSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -55,20 +55,25 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = OpenAIRealtimeSTTService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o-transcribe",
+        settings=OpenAIRealtimeSTTSettings(
-        prompt="Expect words related to dogs, such as breed names.",
+            model="gpt-4o-transcribe",
-        language=Language.EN,
+            prompt="Expect words related to dogs, such as breed names.",
-        # Uses local VAD by default.
+            language=Language.EN,
-        # To enable server-side VAD, set turn_detection=None or
+        ),
-        # a dict with server_vad settings.
-        # turn_detection={"type": "server_vad", "threshold": 0.5},
     )
 
-    tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
+    tts = OpenAITTSService(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        settings=OpenAITTSSettings(
+            voice="ballad",
+        ),
+    )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        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.",
+        settings=OpenAILLMSettings(
+            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.",
+        ),
     )
 
     context = LLMContext()

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

@@ -23,9 +23,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openpipe.llm import OpenPipeLLMService
+from pipecat.services.openpipe.llm import OpenPipeLLMService, OpenPipeLLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -57,7 +57,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     timestamp = int(time.time())
@@ -65,7 +67,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         api_key=os.getenv("OPENAI_API_KEY"),
         openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
         tags={"conversation_id": f"pipecat-{timestamp}"},
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenPipeLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-from pipecat.services.xtts.tts import XTTSService
+from pipecat.services.xtts.tts import XTTSService, XTTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -59,13 +59,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = XTTSService(
             aiohttp_session=session,
-            voice_id="Claribel Dervla",
+            settings=XTTSSettings(
+                voice="Claribel Dervla",
+            ),
             base_url="http://localhost:8000",
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

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

@@ -22,10 +22,10 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
-from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
+from pipecat.services.gladia.config import LanguageConfig
-from pipecat.services.gladia.stt import GladiaSTTService
+from pipecat.services.gladia.stt import GladiaSTTService, GladiaSTTSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -58,7 +58,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = GladiaSTTService(
         api_key=os.getenv("GLADIA_API_KEY", ""),
         region=os.getenv("GLADIA_REGION"),
-        params=GladiaInputParams(
+        settings=GladiaSTTSettings(
             language_config=LanguageConfig(
                 languages=[Language.EN],
             ),
@@ -68,19 +68,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY", ""),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
+    llm = OpenAILLMService(
+        api_key=os.getenv("OPENAI_API_KEY", ""),
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
+    )
 
-    messages = [
+    context = LLMContext()
-        {
-            "role": "system",
-            "content": f"You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(
@@ -114,7 +114,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

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

@@ -22,10 +22,10 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
-from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
+from pipecat.services.gladia.config import LanguageConfig
-from pipecat.services.gladia.stt import GladiaSTTService
+from pipecat.services.gladia.stt import GladiaSTTService, GladiaSTTSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -57,7 +57,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = GladiaSTTService(
         api_key=os.getenv("GLADIA_API_KEY", ""),
         region=os.getenv("GLADIA_REGION"),
-        params=GladiaInputParams(
+        settings=GladiaSTTSettings(
             language_config=LanguageConfig(
                 languages=[Language.EN],
             )
@@ -66,19 +66,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY", ""),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
+    llm = OpenAILLMService(
+        api_key=os.getenv("OPENAI_API_KEY", ""),
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
+    )
 
-    messages = [
+    context = LLMContext()
-        {
-            "role": "system",
-            "content": f"You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
@@ -109,7 +109,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

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

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.lmnt.tts import LmntTTSService
+from pipecat.services.lmnt.tts import LmntTTSService, LmntTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -54,11 +54,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
+    tts = LmntTTSService(
+        api_key=os.getenv("LMNT_API_KEY"),
+        settings=LmntTTSSettings(
+            voice="morgan",
+        ),
+    )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,7 +22,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.groq.llm import GroqLLMService
+from pipecat.services.groq.llm import GroqLLMService, GroqLLMSettings
 from pipecat.services.groq.stt import GroqSTTService
 from pipecat.services.groq.tts import GroqTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -56,8 +56,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = GroqLLMService(
         api_key=os.getenv("GROQ_API_KEY"),
-        model="meta-llama/llama-4-maverick-17b-128e-instruct",
+        settings=GroqLLMSettings(
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            model="meta-llama/llama-4-maverick-17b-128e-instruct",
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     tts = GroqTTSService(api_key=os.getenv("GROQ_API_KEY"))

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

@@ -22,7 +22,7 @@ from pipecat.processors.frameworks.strands_agents import StrandsAgentsProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.aws.stt import AWSTranscribeSTTService
-from pipecat.services.aws.tts import AWSPollyTTSService
+from pipecat.services.aws.tts import AWSPollyTTSService, AWSPollyTTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -95,8 +95,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = AWSPollyTTSService(
         region="us-west-2",  # only specific regions support generative TTS
-        voice_id="Joanna",
+        settings=AWSPollyTTSSettings(
-        params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
+            voice="Joanna",
+            engine="generative",
+            rate="1.1",
+        ),
     )
 
     # Create Strands agent processor

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

@@ -20,9 +20,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.aws.llm import AWSBedrockLLMService
+from pipecat.services.aws.llm import AWSBedrockLLMService, AWSBedrockLLMSettings
 from pipecat.services.aws.stt import AWSTranscribeSTTService
-from pipecat.services.aws.tts import AWSPollyTTSService
+from pipecat.services.aws.tts import AWSPollyTTSService, AWSPollyTTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -54,15 +54,20 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = AWSPollyTTSService(
         region="us-west-2",  # only specific regions support generative TTS
-        voice_id="Joanna",
+        settings=AWSPollyTTSSettings(
-        params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
+            voice="Joanna",
+            engine="generative",
+            rate="1.1",
+        ),
     )
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
         model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
         params=AWSBedrockLLMService.InputParams(temperature=0.8),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=AWSBedrockLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -37,9 +37,9 @@ 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.llm import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService, GoogleLLMSettings
-from pipecat.services.google.stt import GoogleSTTService
+from pipecat.services.google.stt import GoogleSTTService, GoogleSTTSettings
-from pipecat.services.google.tts import GoogleTTSService
+from pipecat.services.google.tts import GoogleTTSService, GoogleTTSSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -70,21 +70,27 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
     stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
+        settings=GoogleSTTSettings(
+            languages=Language.EN_US,
+        ),
     )
 
     tts = GoogleTTSService(
-        voice_id="en-US-Chirp3-HD-Charon",
-        params=GoogleTTSService.InputParams(language=Language.EN_US),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
+        settings=GoogleTTSSettings(
+            voice="en-US-Chirp3-HD-Charon",
+            language=Language.EN_US,
+        ),
     )
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        model="gemini-2.5-flash-image",
+        settings=GoogleLLMSettings(
-        # model="gemini-3-pro-image-preview", # A more powerful model, but slower,
+            model="gemini-2.5-flash-image",
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            # model="gemini-3-pro-image-preview", # A more powerful model, but slower,
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,9 +22,9 @@ 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.llm import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService, GoogleLLMSettings
-from pipecat.services.google.stt import GoogleSTTService
+from pipecat.services.google.stt import GoogleSTTService, GoogleSTTSettings
-from pipecat.services.google.tts import GeminiTTSService
+from pipecat.services.google.tts import GeminiTTSService, GeminiTTSSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -54,15 +54,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot with Gemini TTS")
 
     stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US),
+        settings=GoogleSTTSettings(
+            languages=Language.EN_US,
+        ),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
     )
 
     tts = GeminiTTSService(
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-        model="gemini-2.5-flash-tts",
+        settings=GeminiTTSSettings(
-        voice_id="Charon",
+            model="gemini-2.5-flash-tts",
-        params=GeminiTTSService.InputParams(
+            voice="Charon",
             language=Language.EN_US,
             prompt="You are a helpful AI assistant. Speak in a natural, conversational tone.",
         ),
@@ -71,7 +73,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
         model="gemini-2.5-flash",
-        system_instruction="""You are a helpful AI assistant in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
+        settings=GoogleLLMSettings(
+            system_instruction="""You are a helpful AI assistant in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
 
             IMPORTANT: You're using Gemini TTS which supports expressive markup tags. You can use these tags in your responses:
             - [sigh] - Insert a sigh sound
@@ -89,6 +92,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             - "The answer is... [long pause] ...42!"
 
             Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.""",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,9 +22,9 @@ 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.llm import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService, GoogleLLMSettings
-from pipecat.services.google.stt import GoogleSTTService
+from pipecat.services.google.stt import GoogleSTTService, GoogleSTTSettings
-from pipecat.services.google.tts import GoogleHttpTTSService
+from pipecat.services.google.tts import GoogleHttpTTSService, GoogleHttpTTSSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -54,25 +54,30 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
     stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US, model="chirp_3"),
+        settings=GoogleSTTSettings(
+            languages=Language.EN_US,
+            model="chirp_3",
+        ),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
         location="us",
     )
 
     tts = GoogleHttpTTSService(
-        voice_id="en-US-Chirp3-HD-Charon",
+        settings=GoogleHttpTTSSettings(
-        params=GoogleHttpTTSService.InputParams(language=Language.EN_US),
+            voice="en-US-Chirp3-HD-Charon",
+            language=Language.EN_US,
+        ),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
     )
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        model="gemini-2.5-flash",
+        settings=GoogleLLMSettings(
-        # force a certain amount of thinking if you want it
+            model="gemini-2.5-flash",
-        # params=GoogleLLMService.InputParams(
+            # force a certain amount of thinking if you want it
-        #     thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
+            # thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
-        # ),
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,9 +22,9 @@ 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.llm import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService, GoogleLLMSettings
-from pipecat.services.google.stt import GoogleSTTService
+from pipecat.services.google.stt import GoogleSTTService, GoogleSTTSettings
-from pipecat.services.google.tts import GoogleTTSService
+from pipecat.services.google.tts import GoogleTTSService, GoogleTTSSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -54,25 +54,30 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
     stt = GoogleSTTService(
-        params=GoogleSTTService.InputParams(languages=Language.EN_US, model="chirp_3"),
+        settings=GoogleSTTSettings(
+            languages=Language.EN_US,
+            model="chirp_3",
+        ),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
         location="us",
     )
 
     tts = GoogleTTSService(
-        voice_id="en-US-Chirp3-HD-Charon",
+        settings=GoogleTTSSettings(
-        params=GoogleTTSService.InputParams(language=Language.EN_US),
+            voice="en-US-Chirp3-HD-Charon",
+            language=Language.EN_US,
+        ),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
     )
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        model="gemini-2.5-flash",
+        settings=GoogleLLMSettings(
-        # force a certain amount of thinking if you want it
+            model="gemini-2.5-flash",
-        # params=GoogleLLMService.InputParams(
+            # force a certain amount of thinking if you want it
-        #     thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
+            # thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096),
-        # ),
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,10 +22,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.assemblyai.models import AssemblyAIConnectionParams
+from pipecat.services.assemblyai.stt import AssemblyAISTTService, AssemblyAISTTSettings
-from pipecat.services.assemblyai.stt import AssemblyAISTTService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-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
@@ -94,7 +93,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = AssemblyAISTTService(
         api_key=os.getenv("ASSEMBLYAI_API_KEY"),
         vad_force_turn_endpoint=False,  # Use AssemblyAI's built-in turn detection
-        connection_params=AssemblyAIConnectionParams(
+        settings=AssemblyAISTTSettings(
             speech_model="u3-rt-pro",
             # Optional: Tune turn detection timing (defaults shown below)
             # min_turn_silence=100,  # Default
@@ -108,12 +107,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.assemblyai.stt import AssemblyAISTTService
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -59,12 +59,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -43,9 +43,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -84,12 +84,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.deepgram.tts import DeepgramTTSService
+from pipecat.services.deepgram.tts import DeepgramTTSService, DeepgramTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -58,11 +58,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+    tts = DeepgramTTSService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
+        settings=DeepgramTTSSettings(
+            voice="aura-helios-en",
+        ),
+    )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-from pipecat.services.rime.tts import RimeHttpTTSService
+from pipecat.services.rime.tts import RimeHttpTTSService, RimeTTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -60,14 +60,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = RimeHttpTTSService(
             api_key=os.getenv("RIME_API_KEY", ""),
-            voice_id="luna",
+            settings=RimeTTSSettings(
+                voice="luna",
+                model="arcana",
+            ),
             model="arcana",
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

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

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-from pipecat.services.rime.tts import RimeTTSService
+from pipecat.services.rime.tts import RimeTTSService, RimeTTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = RimeTTSService(
         api_key=os.getenv("RIME_API_KEY", ""),
-        voice_id="luna",
+        settings=RimeTTSSettings(
+            voice="luna",
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,7 +22,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.nvidia.llm import NvidiaLLMService
+from pipecat.services.nvidia.llm import NvidiaLLMService, NvidiaLLMSettings
 from pipecat.services.nvidia.stt import NvidiaSTTService
 from pipecat.services.nvidia.tts import NvidiaTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -56,8 +56,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = NvidiaLLMService(
         api_key=os.getenv("NVIDIA_API_KEY"),
-        model="meta/llama-3.3-70b-instruct",
+        settings=NvidiaLLMSettings(
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            model="meta/llama-3.3-70b-instruct",
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     tts = NvidiaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))

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

@@ -36,8 +36,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.processors.frame_processor import FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService, GoogleLLMSettings
-from pipecat.services.google.tts import GoogleTTSService
+from pipecat.services.google.tts import GoogleTTSService, GoogleTTSSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -216,7 +216,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        model="gemini-2.5-flash",
+        settings=GoogleLLMSettings(
+            model="gemini-2.5-flash",
+        ),
         # force a certain amount of thinking if you want it
         # params=GoogleLLMService.InputParams(
         #     thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
@@ -224,7 +226,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
 
     tts = GoogleTTSService(
-        voice_id="en-US-Chirp3-HD-Charon",
+        settings=GoogleTTSSettings(
+            voice="en-US-Chirp3-HD-Charon",
+            language=Language.EN_US,
+        ),
         params=GoogleTTSService.InputParams(language=Language.EN_US),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
     )

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

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.fish.tts import FishAudioTTSService
+from pipecat.services.fish.tts import FishAudioTTSService, FishAudioTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -57,12 +57,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = FishAudioTTSService(
         api_key=os.getenv("FISH_API_KEY"),
-        model="4ce7e917cedd4bc2bb2e6ff3a46acaa1",  # Barack Obama
+        settings=FishAudioTTSSettings(
+            model="4ce7e917cedd4bc2bb2e6ff3a46acaa1",  # Barack Obama
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.neuphonic.tts import NeuphonicHttpTTSService
+from pipecat.services.neuphonic.tts import NeuphonicHttpTTSService, NeuphonicTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -60,13 +60,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = NeuphonicHttpTTSService(
             api_key=os.getenv("NEUPHONIC_API_KEY"),
-            voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
+            settings=NeuphonicTTSSettings(
+                voice="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
+            ),
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

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

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.neuphonic.tts import NeuphonicTTSService
+from pipecat.services.neuphonic.tts import NeuphonicTTSService, NeuphonicTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = NeuphonicTTSService(
         api_key=os.getenv("NEUPHONIC_API_KEY"),
-        voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
+        settings=NeuphonicTTSSettings(
+            voice="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -23,9 +23,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.fal.stt import FalSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -62,12 +62,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            settings=CartesiaTTSSettings(
+                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            ),
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

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

@@ -21,9 +21,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
     LLMUserAggregatorParams,
 )
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
 
 load_dotenv(override=True)
@@ -44,12 +44,16 @@ async def main():
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.minimax.tts import MiniMaxHttpTTSService
+from pipecat.services.minimax.tts import MiniMaxHttpTTSService, MiniMaxTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -63,12 +63,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             api_key=os.getenv("MINIMAX_API_KEY", ""),
             group_id=os.getenv("MINIMAX_GROUP_ID", ""),
             aiohttp_session=session,
-            params=MiniMaxHttpTTSService.InputParams(language=Language.EN),
+            settings=MiniMaxTTSSettings(
+                language=Language.EN,
+            ),
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

examples/foundational/07z-interruptible-sarvam-http.py

@@ -23,9 +23,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.services.sarvam.stt import SarvamSTTService
-from pipecat.services.sarvam.tts import SarvamHttpTTSService
+from pipecat.services.sarvam.tts import SarvamHttpTTSService, SarvamHttpTTSSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -65,12 +65,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         tts = SarvamHttpTTSService(
             api_key=os.getenv("SARVAM_API_KEY"),
             aiohttp_session=session,
-            params=SarvamHttpTTSService.InputParams(language=Language.EN),
+            settings=SarvamHttpTTSSettings(
+                language=Language.EN,
+            ),
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

examples/foundational/07z-interruptible-sarvam.py

@@ -21,9 +21,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-from pipecat.services.sarvam.stt import SarvamSTTService
+from pipecat.services.sarvam.stt import SarvamSTTService, SarvamSTTSettings
-from pipecat.services.sarvam.tts import SarvamTTSService
+from pipecat.services.sarvam.tts import SarvamTTSService, SarvamTTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -54,17 +54,23 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = SarvamSTTService(
         api_key=os.getenv("SARVAM_API_KEY"),
-        model="saarika:v2.5",
+        settings=SarvamSTTSettings(
+            model="saarika:v2.5",
+        ),
     )
 
     tts = SarvamTTSService(
         api_key=os.getenv("SARVAM_API_KEY"),
-        model="bulbul:v2",
+        settings=SarvamTTSSettings(
-        voice_id="manisha",
+            model="bulbul:v2",
+            voice="manisha",
+        ),
     )
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -22,9 +22,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-from pipecat.services.soniox.stt import SonioxInputParams, SonioxSTTService
+from pipecat.services.soniox.stt import SonioxSTTService, SonioxSTTSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -51,22 +51,28 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = SonioxSTTService(
+    stt = (
-        api_key=os.getenv("SONIOX_API_KEY"),
+        SonioxSTTService(
-        params=SonioxInputParams(
+            api_key=os.getenv("SONIOX_API_KEY"),
-            language_hints=[Language.EN],
+            settings=SonioxSTTSettings(
-            language_hints_strict=True,
+                language_hints=[Language.EN],
+                language_hints_strict=True,
+            ),
         ),
     )
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.inworld.tts import InworldHttpTTSService
+from pipecat.services.inworld.tts import InworldHttpTTSService, InworldTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_output import BaseOutputTransport
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -58,15 +58,19 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         tts = InworldHttpTTSService(
             api_key=os.getenv("INWORLD_API_KEY", ""),
             aiohttp_session=session,
-            voice_id="Ashley",
-            model="inworld-tts-1",
-            # Set to False for non-streaming mode or True for streaming mode.
             streaming=True,
+            settings=InworldTTSSettings(
+                voice="Ashley",
+                model="inworld-tts-1",
+            ),
+            # Set to False for non-streaming mode or True for streaming mode.
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
+            ),
         )
 
         context = LLMContext()

examples/foundational/07zb-interruptible-inworld.py

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.inworld.tts import InworldTTSService
+from pipecat.services.inworld.tts import InworldTTSService, InworldTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_output import BaseOutputTransport
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -56,14 +56,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = InworldTTSService(
         api_key=os.getenv("INWORLD_API_KEY", ""),
-        voice_id="Ashley",
+        settings=InworldTTSSettings(
-        model="inworld-tts-1",
+            voice="Ashley",
-        temperature=1.1,
+            model="inworld-tts-1",
+            temperature=1.1,
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -23,9 +23,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.asyncai.tts import AsyncAIHttpTTSService
+from pipecat.services.asyncai.tts import AsyncAIHttpTTSService, AsyncAITTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -60,13 +60,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = AsyncAIHttpTTSService(
             api_key=os.getenv("ASYNCAI_API_KEY", ""),
-            voice_id=os.getenv("ASYNCAI_VOICE_ID", "e0f39dc4-f691-4e78-bba5-5c636692cc04"),
+            settings=AsyncAITTSSettings(
+                voice="e0f39dc4-f691-4e78-bba5-5c636692cc04",
+            ),
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            settings=OpenAILLMSettings(
+                system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            ),
         )
 
         context = LLMContext()

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

@@ -22,9 +22,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.asyncai.tts import AsyncAITTSService
+from pipecat.services.asyncai.tts import AsyncAITTSService, AsyncAITTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -57,12 +57,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = AsyncAITTSService(
         api_key=os.getenv("ASYNCAI_API_KEY", ""),
-        voice_id=os.getenv("ASYNCAI_VOICE_ID", "e0f39dc4-f691-4e78-bba5-5c636692cc04"),
+        settings=AsyncAITTSSettings(
+            voice="e0f39dc4-f691-4e78-bba5-5c636692cc04",
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -25,9 +25,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -77,12 +77,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/07ze-interruptible-hume.py

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.hume.tts import HUME_SAMPLE_RATE, HumeTTSService
+from pipecat.services.hume.tts import HUME_SAMPLE_RATE, HumeTTSService, HumeTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_output import BaseOutputTransport
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -59,12 +59,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     tts = HumeTTSService(
         api_key=os.getenv("HUME_API_KEY"),
         # Replace with your Hume voice ID
-        voice_id="f898a92e-685f-43fa-985b-a46920f0650b",
+        settings=HumeTTSSettings(
+            voice="f898a92e-685f-43fa-985b-a46920f0650b",
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

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

@@ -21,9 +21,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.gradium.stt import GradiumSTTService
+from pipecat.services.gradium.stt import GradiumSTTService, GradiumSTTSettings
-from pipecat.services.gradium.tts import GradiumTTSService
+from pipecat.services.gradium.tts import GradiumTTSService, GradiumTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -55,20 +55,24 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = GradiumSTTService(
         api_key=os.getenv("GRADIUM_API_KEY"),
         api_endpoint_base_url="wss://us.api.gradium.ai/api/speech/asr",
-        params=GradiumSTTService.InputParams(
+        settings=GradiumSTTSettings(
             language=Language.EN,
         ),
     )
 
     tts = GradiumTTSService(
         api_key=os.getenv("GRADIUM_API_KEY"),
-        voice_id="YTpq7expH9539ERJ",
         url="wss://us.api.gradium.ai/api/speech/tts",
+        settings=GradiumTTSSettings(
+            voice="YTpq7expH9539ERJ",
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/07zg-interruptible-camb.py

@@ -21,9 +21,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.camb.tts import CambTTSService
+from pipecat.services.camb.tts import CambTTSService, CambTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CambTTSService(
         api_key=os.getenv("CAMB_API_KEY"),
-        model="mars-flash",
+        settings=CambTTSSettings(
+            model="mars-flash",
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful voice assistant powered by Camb AI text-to-speech. ",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful voice assistant powered by Camb AI text-to-speech. ",
+        ),
     )
 
     context = LLMContext()

examples/foundational/07zi-interruptible-piper.py

@@ -22,8 +22,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-from pipecat.services.piper.tts import PiperTTSService
+from pipecat.services.piper.tts import PiperTTSService, PiperTTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -54,11 +54,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = PiperTTSService(voice_id="en_US-ryan-high")
+    tts = PiperTTSService(
+        settings=PiperTTSSettings(
+            voice="en_US-ryan-high",
+        ),
+    )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/07zj-interruptible-kokoro.py

@@ -22,8 +22,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.kokoro.tts import KokoroTTSService
+from pipecat.services.kokoro.tts import KokoroTTSService, KokoroTTSSettings
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -54,11 +54,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = KokoroTTSService(voice_id="af_heart")
+    tts = KokoroTTSService(
+        settings=KokoroTTSSettings(
+            voice="af_heart",
+        ),
+    )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/07zk-interruptible-resemble.py

@@ -22,8 +22,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
-from pipecat.services.resembleai.tts import ResembleAITTSService
+from pipecat.services.resembleai.tts import ResembleAITTSService, ResembleAITTSSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -59,12 +59,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = ResembleAITTSService(
         api_key=os.getenv("RESEMBLE_API_KEY"),
-        voice_id=os.getenv("RESEMBLE_VOICE_UUID"),
+        settings=ResembleAITTSSettings(
+            voice=os.getenv("RESEMBLE_VOICE_UUID"),
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/08-custom-frame-processor.py

@@ -26,9 +26,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
@@ -95,12 +95,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/10-wake-phrase.py

@@ -22,9 +22,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -56,12 +56,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful assistant. Respond to what the user said in a creative and helpful way. Keep your responses brief.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful assistant. Respond to what the user said in a creative and helpful way. Keep your responses brief.",
+        ),
     )
 
     hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])

examples/foundational/11-sound-effects.py

@@ -30,9 +30,9 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.logger import FrameLogger
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -106,12 +106,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     context = LLMContext()

examples/foundational/12-describe-image-openai.py

@@ -23,9 +23,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
@@ -53,12 +53,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/12a-describe-image-anthropic.py

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.anthropic.llm import AnthropicLLMService, AnthropicLLMSettings
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -53,12 +53,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = AnthropicLLMService(
         api_key=os.getenv("ANTHROPIC_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
+        settings=AnthropicLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/12b-describe-image-aws.py

@@ -23,8 +23,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.aws.llm import AWSBedrockLLMService
+from pipecat.services.aws.llm import AWSBedrockLLMService, AWSBedrockLLMSettings
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -53,17 +53,21 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        settings=AWSBedrockLLMSettings(
-        # Note: usually, prefer providing latency="optimized" param.
+            model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
-        # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
+            # Note: usually, prefer providing latency="optimized" param.
-        # which we need for image input.
+            # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+            # which we need for image input.
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
+            params=AWSBedrockLLMService.InputParams(temperature=0.8),
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/12c-describe-image-gemini-flash.py

@@ -23,9 +23,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService, GoogleLLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
@@ -53,12 +53,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
+        settings=GoogleLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
+        ),
     )
 
     context = LLMContext()

examples/foundational/12d-describe-image-moondream.py

@@ -17,7 +17,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.moondream.vision import MoondreamService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -42,7 +42,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     vision = MoondreamService()

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

@@ -16,7 +16,7 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService, Language, LiveOptions
+from pipecat.services.deepgram.stt import DeepgramSTTService, DeepgramSTTSettings, Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -49,7 +49,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(
         api_key=os.getenv("DEEPGRAM_API_KEY"),
-        live_options=LiveOptions(language=Language.EN),
+        settings=DeepgramSTTSettings(
+            language=Language.EN,
+        ),
     )
 
     tl = TranscriptionLogger()

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

@@ -50,7 +50,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = GladiaSTTService(
         api_key=os.getenv("GLADIA_API_KEY"),
         region=os.getenv("GLADIA_REGION"),
-        # live_options=LiveOptions(language=Language.FR),
+        # settings=GladiaSTTSettings(
+        #     language_config=LanguageConfig(
+        #         languages=[Language.FR],
+        #     ),
+        # ),
     )
 
     tl = TranscriptionLogger()

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

@@ -22,7 +22,7 @@ from pipecat.services.gladia.config import (
     RealtimeProcessingConfig,
     TranslationConfig,
 )
-from pipecat.services.gladia.stt import GladiaSTTService
+from pipecat.services.gladia.stt import GladiaSTTService, GladiaSTTSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -59,16 +59,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = GladiaSTTService(
         api_key=os.getenv("GLADIA_API_KEY"),
         region=os.getenv("GLADIA_REGION"),
-        params=GladiaInputParams(
+        settings=GladiaSTTSettings(
             language_config=LanguageConfig(
-                languages=[Language.EN],  # Input in English
+                languages=[Language.EN],
                 code_switching=False,
             ),
             realtime_processing=RealtimeProcessingConfig(
-                translation=True,  # Enable translation
+                translation=True,
                 translation_config=TranslationConfig(
-                    target_languages=[Language.ES],  # Translate to Spanish
+                    target_languages=[Language.ES],
-                    model="enhanced",  # Use the enhanced translation model
+                    model="enhanced",
                 ),
             ),
         ),

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

@@ -16,8 +16,7 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.assemblyai.models import AssemblyAIConnectionParams
+from pipecat.services.assemblyai.stt import AssemblyAISTTService, AssemblyAISTTSettings
-from pipecat.services.assemblyai.stt import AssemblyAISTTService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -50,8 +49,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = AssemblyAISTTService(
         api_key=os.getenv("ASSEMBLYAI_API_KEY"),
-        connection_params=AssemblyAIConnectionParams(
+        settings=AssemblyAISTTSettings(
-            speech_model="u3-rt-pro",
+            model="u3-rt-pro",
         ),
     )
 

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

@@ -18,7 +18,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.whisper.stt import MLXModel, WhisperSTTServiceMLX
+from pipecat.services.whisper.stt import MLXModel, WhisperMLXSTTSettings, WhisperSTTServiceMLX
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -77,7 +77,11 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = WhisperSTTServiceMLX(model=MLXModel.LARGE_V3_TURBO)
+    stt = WhisperSTTServiceMLX(
+        settings=WhisperMLXSTTSettings(
+            model=MLXModel.LARGE_V3_TURBO,
+        ),
+    )
 
     tl = TranscriptionLogger()
 

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

@@ -19,7 +19,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.sambanova.stt import SambaNovaSTTService
+from pipecat.services.sambanova.stt import SambaNovaSTTService, SambaNovaSTTSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -79,7 +79,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
     stt = SambaNovaSTTService(
-        model="Whisper-Large-v3",
+        settings=SambaNovaSTTSettings(
+            model="Whisper-Large-v3",
+        ),
         api_key=os.getenv("SAMBANOVA_API_KEY"),
     )
 

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

@@ -16,7 +16,7 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
+from pipecat.services.speechmatics.stt import SpeechmaticsSTTService, SpeechmaticsSTTSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -65,7 +65,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = SpeechmaticsSTTService(
         api_key=os.getenv("SPEECHMATICS_API_KEY"),
-        params=SpeechmaticsSTTService.InputParams(
+        settings=SpeechmaticsSTTSettings(
             language=Language.EN,
             speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
         ),

examples/foundational/13l-gradium-transcription.py

@@ -16,7 +16,7 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.gradium.stt import GradiumSTTService
+from pipecat.services.gradium.stt import GradiumSTTService, GradiumSTTSettings
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -52,7 +52,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = GradiumSTTService(
         api_key=os.getenv("GRADIUM_API_KEY"),
         api_endpoint_base_url="wss://us.api.gradium.ai/api/speech/asr",
-        params=GradiumSTTService.InputParams(language=Language.EN, delay_in_frames=8),
+        settings=GradiumSTTSettings(
+            language=Language.EN,
+            delay_in_frames=8,
+        ),
     )
 
     tl = TranscriptionLogger()

examples/foundational/13m-openai-transcription.py

@@ -18,7 +18,7 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.openai.stt import OpenAIRealtimeSTTService
+from pipecat.services.openai.stt import OpenAIRealtimeSTTService, OpenAIRealtimeSTTSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -53,8 +53,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = OpenAIRealtimeSTTService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o-transcribe",
+        settings=OpenAIRealtimeSTTSettings(
-        prompt="Expect words related to dogs, such as breed names.",
+            model="gpt-4o-transcribe",
+            prompt="Expect words related to dogs, such as breed names.",
+        ),
     )
 
     tl = TranscriptionLogger()

examples/foundational/14-function-calling.py

@@ -23,10 +23,10 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -67,12 +67,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
 
     # You can also register a function_name of None to get all functions

examples/foundational/14a-function-calling-anthropic.py

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.anthropic.llm import AnthropicLLMService, AnthropicLLMSettings
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -69,10 +69,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
-    llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
+    llm = AnthropicLLMService(
+        api_key=os.getenv("ANTHROPIC_API_KEY"),
+        settings=AnthropicLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
+    )
     llm.register_function("get_weather", get_weather)
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
@@ -100,16 +107,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
     tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
 
-    # todo: test with very short initial user message
+    context = LLMContext(tools=tools)
-
-    # messages = [{"role": "system",
-    #              "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation."},
-    #             {"role": "user",
-    #              "content": " Start the conversation by introducing yourself."}]
-
-    messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}]
-
-    context = LLMContext(messages, tools)
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),

examples/foundational/14c-function-calling-together.py

@@ -24,10 +24,10 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.together.llm import TogetherLLMService
+from pipecat.services.together.llm import TogetherLLMService, TogetherLLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -64,13 +64,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = TogetherLLMService(
         api_key=os.getenv("TOGETHER_API_KEY"),
-        model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
+        settings=TogetherLLMSettings(
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+            model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.

examples/foundational/14d-function-calling-anthropic-video.py

@@ -28,8 +28,8 @@ from pipecat.runner.utils import (
     get_transport_client_id,
     maybe_capture_participant_camera,
 )
-from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.anthropic.llm import AnthropicLLMService, AnthropicLLMSettings
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -90,13 +90,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     # Anthropic for vision analysis
     llm = AnthropicLLMService(
         api_key=os.getenv("ANTHROPIC_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+        settings=AnthropicLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+        ),
     )
     llm.register_function("fetch_user_image", fetch_user_image)
 

examples/foundational/14d-function-calling-aws-video.py

@@ -28,8 +28,8 @@ from pipecat.runner.utils import (
     get_transport_client_id,
     maybe_capture_participant_camera,
 )
-from pipecat.services.aws.llm import AWSBedrockLLMService
+from pipecat.services.aws.llm import AWSBedrockLLMService, AWSBedrockLLMSettings
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -90,18 +90,22 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     # AWS for vision analysis
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        settings=AWSBedrockLLMSettings(
-        # Note: usually, prefer providing latency="optimized" param.
+            model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
-        # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
+            # Note: usually, prefer providing latency="optimized" param.
-        # which we need for image input.
+            # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+            # which we need for image input.
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+            temperature=0.8,
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+        ),
     )
     llm.register_function("fetch_user_image", fetch_user_image)
 

examples/foundational/14d-function-calling-gemini-flash-video.py

@@ -28,9 +28,9 @@ from pipecat.runner.utils import (
     get_transport_client_id,
     maybe_capture_participant_camera,
 )
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService, GoogleLLMSettings
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -90,13 +90,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     # Google Gemini model for vision analysis
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+        settings=GoogleLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+        ),
     )
     llm.register_function("fetch_user_image", fetch_user_image)
 

examples/foundational/14d-function-calling-moondream-video.py

@@ -37,11 +37,11 @@ from pipecat.runner.utils import (
     get_transport_client_id,
     maybe_capture_participant_camera,
 )
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.services.moondream.vision import MoondreamService
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
@@ -121,12 +121,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+        ),
     )
     llm.register_function("fetch_user_image", fetch_user_image)
 

examples/foundational/14d-function-calling-openai-video.py

@@ -29,10 +29,10 @@ from pipecat.runner.utils import (
     get_transport_client_id,
     maybe_capture_participant_camera,
 )
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.openai.llm import OpenAILLMService, OpenAILLMSettings
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
@@ -91,12 +91,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+        settings=OpenAILLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way. You are able to describe images from the user camera.",
+        ),
     )
     llm.register_function("fetch_user_image", fetch_user_image)
 

examples/foundational/14e-function-calling-google.py

@@ -29,9 +29,9 @@ from pipecat.runner.utils import (
     get_transport_client_id,
     maybe_capture_participant_camera,
 )
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.google.llm import GoogleLLMService
+from pipecat.services.google.llm import GoogleLLMService, GoogleLLMSettings
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -100,10 +100,36 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
-    llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
+    system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
+
+Your response will be turned into speech so use only simple words and punctuation.
+
+You have access to three tools: get_weather, get_restaurant_recommendation, and get_image.
+
+You can respond to questions about the weather using the get_weather tool.
+
+You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
+indicate you should use the get_image tool are:
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
+"""
+
+    llm = GoogleLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        settings=GoogleLLMSettings(
+            system_instruction=system_prompt,
+        ),
+    )
     llm.register_function("get_weather", get_weather)
     llm.register_function("get_image", get_image)
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
@@ -156,29 +182,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     )
     tools = ToolsSchema(standard_tools=[weather_function, get_image_function, restaurant_function])
 
-    system_prompt = """\
+    context = LLMContext(tools=tools)
-You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
-
-Your response will be turned into speech so use only simple words and punctuation.
-
-You have access to three tools: get_weather, get_restaurant_recommendation, and get_image.
-
-You can respond to questions about the weather using the get_weather tool.
-
-You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
-indicate you should use the get_image tool are:
-- What do you see?
-- What's in the video?
-- Can you describe the video?
-- Tell me about what you see.
-- Tell me something interesting about what you see.
-- What's happening in the video?
-"""
-    messages = [
-        {"role": "system", "content": system_prompt},
-    ]
-
-    context = LLMContext(messages, tools)
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
@@ -214,9 +218,9 @@ indicate you should use the get_image tool are:
         client_id = get_transport_client_id(transport, client)
 
         # Kick off the conversation.
-        messages.append(
+        context.add_message(
             {
-                "role": "system",
+                "role": "user",
                 "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
             }
         )

examples/foundational/14f-function-calling-groq.py

@@ -24,8 +24,8 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
-from pipecat.services.groq.llm import GroqLLMService
+from pipecat.services.groq.llm import GroqLLMService, GroqLLMSettings
 from pipecat.services.groq.stt import GroqSTTService
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -64,12 +64,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = GroqLLMService(
         api_key=os.getenv("GROQ_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=GroqLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.

examples/foundational/14g-function-calling-grok.py

@@ -24,9 +24,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
 from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.grok.llm import GrokLLMService
+from pipecat.services.grok.llm import GrokLLMService, GrokLLMSettings
 from pipecat.services.llm_service import FunctionCallParams
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -64,12 +64,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        settings=CartesiaTTSSettings(
+            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        ),
     )
 
     llm = GrokLLMService(
         api_key=os.getenv("GROK_API_KEY"),
-        system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        settings=GrokLLMSettings(
+            system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        ),
     )
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.