> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pipecat.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Service Settings
> Learn how to configure and update AI service settings at initialization and runtime.
Settings are the runtime-configurable properties of AI services, including things like the model, voice, language, temperature, and other provider-specific options. Every service exposes a `Settings` class that you can use in two ways:
1. **Configure at initialization**: pass a `settings=` argument when constructing a service.
2. **Update at runtime**: push an `*UpdateSettingsFrame` through the pipeline to change settings mid-conversation.
## Configuring settings at initialization
Pass a `settings=` argument to any service constructor using that service's `Settings` class:
```python theme={null}
from pipecat.services.openai.llm import OpenAILLMService
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
settings=OpenAILLMService.Settings(
model="gpt-4o",
temperature=0.7,
system_instruction="You are a helpful assistant.",
),
)
```
```python theme={null}
from pipecat.services.cartesia.tts import CartesiaTTSService
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
settings=CartesiaTTSService.Settings(
voice="71a7ad14-091c-4e8e-a314-022ece01c121",
),
)
```
```python theme={null}
from pipecat.services.deepgram.stt import DeepgramSTTService
stt = DeepgramSTTService(
api_key=os.getenv("DEEPGRAM_API_KEY"),
settings=DeepgramSTTService.Settings(
model="nova-3",
language="en",
smart_format=True,
),
)
```
You only need to specify the settings you want to override. Everything else uses the service's defaults.
## Common settings by service type
Each service type has a base set of settings. Individual services may extend these with provider-specific fields.
### LLM settings
| Setting | Type | Description |
| -------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `model` | `str` | Model identifier (e.g. `"gpt-4o"`, `"claude-sonnet-4-5-20250929"`) |
| `system_instruction` | `str` | System prompt for the model. See [Context Management](/pipecat/learn/context-management#system-instruction-developer-messages-and-system-messages) for how this interacts with developer messages and context system messages. |
| `temperature` | `float` | Sampling temperature |
| `max_tokens` | `int` | Maximum tokens to generate |
| `top_p` | `float` | Nucleus sampling probability |
| `top_k` | `int` | Top-k sampling parameter |
| `frequency_penalty` | `float` | Frequency penalty |
| `presence_penalty` | `float` | Presence penalty |
| `seed` | `int` | Random seed for reproducibility |
### TTS settings
| Setting | Type | Description |
| ---------- | ----------------- | ----------------------------- |
| `model` | `str` | TTS model identifier |
| `voice` | `str` | Voice identifier or name |
| `language` | `Language \| str` | Language for speech synthesis |
### STT settings
| Setting | Type | Description |
| ---------- | ----------------- | ------------------------------- |
| `model` | `str` | STT model identifier |
| `language` | `Language \| str` | Language for speech recognition |
Individual services extend these base settings with provider-specific fields.
For example, Deepgram STT adds `endpointing`, `smart_format`, `diarize`, and
more. See the individual [service
documentation](/api-reference/server/services/supported-services) for the full list of
available settings.
## Updating settings at runtime
You can change service settings while the pipeline is running by pushing an update settings frame. This is useful for scenarios like switching languages, changing voices, or adjusting LLM parameters mid-conversation.
Use these frame types:
| Frame | Target |
| ------------------------ | ------------ |
| `LLMUpdateSettingsFrame` | LLM services |
| `TTSUpdateSettingsFrame` | TTS services |
| `STTUpdateSettingsFrame` | STT services |
Only include the fields you want to change. Unspecified fields are left as-is.
```python theme={null}
from pipecat.frames.frames import TTSUpdateSettingsFrame
# Change TTS voice mid-conversation
await task.queue_frame(
TTSUpdateSettingsFrame(
delta=CartesiaTTSService.Settings(voice="new-voice-id")
)
)
```
```python theme={null}
from pipecat.frames.frames import LLMUpdateSettingsFrame
# Lower the temperature for more deterministic responses
await task.queue_frame(
LLMUpdateSettingsFrame(
delta=OpenAILLMService.Settings(temperature=0.2)
)
)
```
```python theme={null}
from pipecat.frames.frames import STTUpdateSettingsFrame
from pipecat.transcriptions.language import Language
# Switch STT language to Spanish
await task.queue_frame(
STTUpdateSettingsFrame(
delta=DeepgramSTTService.Settings(language=Language.ES)
)
)
```
Update settings frames are uninterruptible. They will always be processed even
if a user interruption occurs.
## Service-specific settings
Services can extend the base settings with provider-specific fields. For example:
* **Cartesia TTS** adds `generation_config` (volume, speed, emotion) and `pronunciation_dict_id`
* **OpenAI TTS** adds `instructions` and `speed`
* **Deepgram STT** adds `endpointing`, `smart_format`, `diarize`, `punctuate`, and many more
* **OpenAI LLM** adds `max_completion_tokens`
These service-specific fields work the same way — set them at initialization or update them at runtime:
```python theme={null}
from pipecat.services.cartesia.tts import CartesiaTTSService, GenerationConfig
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
settings=CartesiaTTSService.Settings(
voice="71a7ad14-091c-4e8e-a314-022ece01c121",
generation_config=GenerationConfig(speed=1.2, emotion="excited"),
),
)
```
See the individual service documentation for a complete list of available settings:
## Passing extra parameters
Every Settings class includes an `extra` dict for passing provider-specific parameters that Pipecat doesn't have a dedicated field for. This is useful when a provider supports options that haven't been explicitly added to the Settings dataclass yet:
```python theme={null}
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
settings=OpenAILLMService.Settings(
model="gpt-4o",
extra={"logprobs": True, "top_logprobs": 5},
),
)
```
Values in `extra` are passed through to the underlying API call. You can also update `extra` at runtime:
```python theme={null}
await task.queue_frame(
LLMUpdateSettingsFrame(
delta=OpenAILLMService.Settings(
extra={"logprobs": False},
)
)
)
```
The `extra` dict is merged on updates — new keys are added and existing keys
are overwritten, but keys not present in the delta are left unchanged.
## Migration from InputParams
The `InputParams` / `params=` pattern is deprecated as of v0.0.105. Use `Settings` / `settings=` instead.
**Before:**
```python theme={null}
llm = OpenAILLMService(
model="gpt-4o",
params=OpenAILLMService.InputParams(
temperature=0.7,
),
)
```
**After:**
```python theme={null}
llm = OpenAILLMService(
settings=OpenAILLMService.Settings(
model="gpt-4o",
temperature=0.7,
),
)
```
Note that `model` has moved from a top-level constructor argument into `Settings`. Both the old and new patterns still work during the deprecation period, but `settings` values take precedence when both are provided.