Always create UserIdleController (timeout=0 means disabled), removing
all Optional guards. Add UserIdleTimeoutUpdateFrame to allow changing
the idle timeout at runtime.
Replace the continuous heartbeat-based timer (UserSpeakingFrame/BotSpeakingFrame
+ asyncio.Event loop) with a simple one-shot timer that starts when
BotStoppedSpeakingFrame is received and cancels on UserStartedSpeakingFrame or
BotStartedSpeakingFrame. This eliminates false idle triggers caused by gaps
between the user finishing speaking and the bot starting to speak (LLM/TTS
latency).
Guard the timer start with two conditions to prevent false triggers:
- User turn in progress: during interruptions, BotStoppedSpeaking arrives
while the user is still speaking mid-turn.
- Function calls in progress: FunctionCallsStarted arrives before
BotStoppedSpeaking because the bot speaks concurrently with the function
call starting, so the timer must wait for the result and subsequent bot
response.
Now that all services use typed `ServiceSettings` objects, this removes the interim scaffolding that supported both dict-based and typed settings paths in parallel. Specifically: removes old dict-based `_update_settings(settings: Mapping)` methods from base classes, removes `isinstance(self._settings, ServiceSettings)` guards, simplifies `process_frame` branching, and renames `_update_settings_from_typed` to `_update_settings` across all ~30 service implementations. Also renames the no-arg `_update_settings()` helper on realtime services to `_send_session_update()` to avoid collision, adds `from_mapping` overrides on `GoogleLLMSettings` and `AnthropicLLMSettings` for ThinkingConfig dict-to-object conversion, and replaces a broken no-arg `_update_settings()` call in Gemini Live with a TODO.
- NvidiaSTTService.set_model: convert to proper DeprecationWarning (model can't change at runtime for Riva streaming STT)
- NvidiaTTSService.set_model: same treatment for Riva TTS
- NvidiaSegmentedSTTService.set_model: remove — base class now routes through _update_settings_from_typed which re-creates the recognition config
- GeminiTTSService.set_voice: remove — move AVAILABLE_VOICES validation into _update_settings_from_typed so it fires on both legacy and new paths
Standardize all STT, TTS, and LLM service classes to declare `_settings` with the narrowed Settings type as a class-level annotation. This gives editors and type checkers the specific type when hovering or autocompleting on `self._settings` in each service and its subclasses. Inline `self._settings: Type = ...` assignments are replaced with plain `self._settings = ...`.
`filter_incomplete_user_turns` and `user_turn_completion_config` were only handled in the legacy dict-based `_update_settings` code path. This adds them to `LLMSettings` and introduces `LLMService._update_settings_from_typed` so the typed path handles them too.
Does not (yet) touch `InputParams`, to avoid scope creep and touching something currently part of the public API. But there is a lot of overlap between `*Settings` object fields and `InputParams` fields.
Other than discoverability/typing, these are some other improvements brought by this refactor:
- There is now a single code path (see `_update_settings_from_typed`) where services can respond to settings changes (by, say, reconnecting if needed), improving maintainability and guaranteeing one and only one reconnection no matter which settings changed
- `set_language`/`set_model`/`set_voice`—which we're assuming are usable as public methods, though *not* recommended over `*UpdateSettingsFrame`—all use the same code path as settings updates. They're also now all consistent in that, if a service needs to respond to a change (by, say, reconnecting if needed), any of these methods will kick off that process. Note that this is technically a behavior change.
- Several services now properly react to changed settings by reconnecting:
- `AWSTranscribeSTTService`
- `AzureSTTService`
- `SonioxSTTService`
- `GladiaSTTService`
- `SpeechmaticsSTTService`
- `AssemblyAISTTService`
- `CartesiaSTTService`
- `FishAudioTTSService` (would previously only reconnect when `model` changed)
- `GoogleSTTService`
- `SpeechmaticsSTTService` (which previously only handled *some* settings updates through a nonstandard public `update_params` method)
- `GradiumSTTService`
- `NvidiaSegmentedSTTService` (which previously only handled changes to language)
- Bookkeeping across various services has been reduced, mostly by deduping ivars; the `self._settings` ivar is treated as the source of truth
NOTE: I pretty much guarantee that there are services missed in this PR in terms of bringing to consistency with how updates are handled (like whether changes in certain fields trigger reconnects when they need to). We can squash remaining inconsistencies as we stumble onto them, service by service. The goal here is to get things *mostly* in order, and establish the infrastructure and patterns we'll need going forward.
The outer try/except in each service decorator caught both tracing
setup errors and application errors from the wrapped function. If the
function itself raised (e.g. LLM rate limit, TTS timeout), the
exception was caught and the function was called a second time.
Fix by tracking whether the original function was called via a
fn_called flag. If the function was already called, re-raise the
exception instead of falling back to untraced re-execution.
Adds a Claude Code skill that analyzes the current branch diff against
main, maps changed source files to their doc pages, and makes targeted
updates to Configuration, InputParams, Usage, Notes, and Event Handlers
sections.
The class_decorators.py module (Traceable, @traceable, @traced) is not
used anywhere in the codebase. Mark it deprecated and fix the misleading
comment in service_decorators.py that referenced it as if it were active.
Consolidate _tracing_enabled and _tracing_context from LLMService,
STTService, and TTSService into the shared AIService base class.
Extract _get_turn_context() helper in service_decorators.py to
encapsulate the repeated pattern across all traced decorators.
Add model-specific params (arcana: repetition_penalty, temperature, top_p;
mistv2: no_text_normalization, save_oovs, segment) with dynamic query param
building via _build_settings(). Model/voice/param changes now trigger
WebSocket reconnection since all settings are URL query params.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This allows non-websocket STT services (like SarvamSTTService, which uses
the Sarvam Python SDK for connection management) to reuse the same keepalive
pattern. Subclasses override _send_keepalive() and _is_keepalive_ready() for
their specific protocol.
