Fix copyright

Run the content filter concurrently with LLM text generation
using ParallelPipeline, with a ContentFilterGate that blocks
output until the filter approves or rejects the content.

.claude/skills/changelog/SKILL.md

@@ -32,20 +32,6 @@ Create changelog files for the important commits in this PR. The PR number is pr
 
 6. Use ⚠️ emoji prefix for breaking changes.
 
-7. **Write changes in user-facing terms first.** Lead with what users of the framework will notice: new APIs, changed behavior, new parameters, fixed bugs they might have hit, etc. Implementation details (internal refactoring, how something is wired up under the hood) can be included as secondary context after the user-facing description, but should never be the *only* content of a changelog entry when there is a user-visible effect.
-
-   **Good** (user-facing first, implementation detail as context):
-   ```
-   - Turn completion instructions now persist correctly across full context updates when using `system_instruction`. Previously they were injected as a context system message, which caused warning spam and didn't survive context updates.
-   ```
-
-   **Bad** (implementation detail only, no user-facing framing):
-   ```
-   - Fixed turn completion instructions being injected as a context system message instead of using `system_instruction`.
-   ```
-
-   Ask yourself: "If I'm a developer building on Pipecat, what would I notice changed?" Start there.
-
 ## Example
 
 For PR #3519 with a new feature and a bug fix:
@@ -57,5 +43,5 @@ For PR #3519 with a new feature and a bug fix:
 
 `changelog/3519.fixed.md`:
 ```
-- Fixed an issue where something was not working correctly in some user-visible scenario. The root cause was an internal implementation detail.
+- Fixed an issue where something was not working correctly.
 ```

CHANGELOG.md

@@ -7,883 +7,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 <!-- towncrier release notes start -->
 
-## [0.0.108] - 2026-03-27
-
-### Added
-
-- Added `SarvamLLMService` with support for `sarvam-30b`, `sarvam-30b-16k`,
-  `sarvam-105b` and `sarvam-105b-32k`.
-  (PR [#3978](https://github.com/pipecat-ai/pipecat/pull/3978))
-
-- Added `on_turn_context_created(context_id)` hook to `TTSService`. Override
-  this to perform provider-specific setup (e.g. eagerly opening a server-side
-  context) before text starts flowing. Called each time a new turn context ID
-  is created.
-  (PR [#4013](https://github.com/pipecat-ai/pipecat/pull/4013))
-
-- Added `XAIHttpTTSService` for text-to-speech using xAI's HTTP TTS API.
-  (PR [#4031](https://github.com/pipecat-ai/pipecat/pull/4031))
-
-- Added support for "developer" role messages in conversation context across
-  all LLM adapters. For non-OpenAI services (Anthropic, Google, AWS Bedrock),
-  "developer" messages are converted to "user" messages (use
-  `system_instruction` to set the system instruction). For OpenAI services,
-  "developer" messages pass through in conversation history. For the Responses
-  API, they are kept as "developer" role (matching the existing "system" →
-  "developer" conversion).
-  (PR [#4089](https://github.com/pipecat-ai/pipecat/pull/4089))
-
-- Added `SmallestTTSService`, a WebSocket-based TTS service integration with
-  Smallest AI's Waves API. Supports the Lightning v2 and v3.1 models with
-  configurable voice, language, speed, consistency, similarity, and enhancement
-  settings.
-  (PR [#4092](https://github.com/pipecat-ai/pipecat/pull/4092))
-
-- Added warnings in turn stop strategies when `VADParams.stop_secs` differs
-  from the recommended default (0.2s) or when `stop_secs >= STT p99 latency`,
-  which collapses the STT wait timeout to 0s and may cause delayed turn
-  detection. The warnings guide developers to re-run the
-  [stt-benchmark](https://github.com/pipecat-ai/stt-benchmark) with their VAD
-  settings.
-  (PR [#4115](https://github.com/pipecat-ai/pipecat/pull/4115))
-
-- Added `domain` parameter to `AssemblyAISTTSettings` for specialized
-  recognition modes such as Medical Mode (`domain="medical-v1"`).
-  (PR [#4117](https://github.com/pipecat-ai/pipecat/pull/4117))
-
-- Added `NovitaLLMService` for using Novita AI's LLM models via their
-  OpenAI-compatible API.
-  (PR [#4119](https://github.com/pipecat-ai/pipecat/pull/4119))
-
-- Added `cleanup()` method to `VADAnalyzer` and `VADController` so VAD analyzer
-  resources are properly released when no longer needed. Custom `VADAnalyzer`
-  subclasses can override `cleanup()` to free any held resources.
-  (PR [#4120](https://github.com/pipecat-ai/pipecat/pull/4120))
-
-- Added `on_end_of_turn` event handler to `AssemblyAISTTService`. This fires
-  after the final transcript is pushed, providing a reliable hook for
-  end-of-turn logic that doesn't race with `TranscriptionFrame`. Works in both
-  Pipecat and AssemblyAI turn detection modes.
-  (PR [#4128](https://github.com/pipecat-ai/pipecat/pull/4128))
-
-- Added `DeepgramFluxSageMakerSTTService` for running Deepgram Flux
-  speech-to-text on AWS SageMaker endpoints.  Use with
-  `ExternalUserTurnStrategies` to take advantage of Flux's turn detection.
-  (PR [#4143](https://github.com/pipecat-ai/pipecat/pull/4143))
-
-- Added `Mem0MemoryService.get_memories()` convenience method for retrieving
-  all stored memories outside the pipeline (e.g. to build a personalized
-  greeting at connection time). This avoids the need to manually handle client
-  type branching, filter construction, and async wrapping.
-  (PR [#4156](https://github.com/pipecat-ai/pipecat/pull/4156))
-
-### Changed
-
-- Added context prewarming path for `InworldTTSService` to improve first audio
-  latency.
-  (PR [#4013](https://github.com/pipecat-ai/pipecat/pull/4013))
-
-- Added `KrispVivaVadAnalyzer` for Voice Activity Detection using the Krisp
-  VIVA SDK (requires `krisp_audio`).
-  (PR [#4022](https://github.com/pipecat-ai/pipecat/pull/4022))
-
-- Modified `InworldTTSService` to close context at end of turn instead of
-  relying on idle timeout.
-  (PR [#4028](https://github.com/pipecat-ai/pipecat/pull/4028))
-
-- Added Gemini 3 support to the Gemini Live service.
-  (PR [#4078](https://github.com/pipecat-ai/pipecat/pull/4078))
-
-- `TTSService`: the default `stop_frame_timeout_s` (idle time before an
-  automatic `TTSStoppedFrame` is pushed when `push_stop_frames=True`) has
-  changed from `2.0` to `3.0` seconds.
-  (PR [#4084](https://github.com/pipecat-ai/pipecat/pull/4084))
-
-- ⚠️ `GeminiLLMAdapter` now only treats `messages[0]` as the initial system
-  message, matching all other adapters. Previously it searched for the first
-  "system" message anywhere in the conversation history. A "system" message
-  appearing later in the list will now be converted to "user" instead of being
-  extracted as the system instruction.
-  (PR [#4089](https://github.com/pipecat-ai/pipecat/pull/4089))
-
-- Fixed `InworldTtsService` to fallback to full text when TTS timestamps are
-  not received.
-  (PR [#4113](https://github.com/pipecat-ai/pipecat/pull/4113))
-
-- ⚠️ Realtime services (Gemini Live, OpenAI Realtime, Grok Realtime, Nova
-  Sonic) now prefer `system_instruction` from service settings over an initial
-  system message in the LLM context, matching the behavior of non-realtime
-  services. Previously, context-provided system instructions took precedence. A
-  warning is now logged when both are set.
-  (PR [#4130](https://github.com/pipecat-ai/pipecat/pull/4130))
-
-- Bumped `nvidia-riva-client` minimum version to `>=2.25.1`.
-  (PR [#4136](https://github.com/pipecat-ai/pipecat/pull/4136))
-
-- Upgraded `protobuf` from 5.x to 6.x (`>=6.31.1,<7`).
-  (PR [#4136](https://github.com/pipecat-ai/pipecat/pull/4136))
-
-- Unrecognized language strings (e.g. Deepgram's `"multi"`) no longer produce a
-  warning at startup. The log message has been downgraded to debug level since
-  these are valid service-specific values that are passed through correctly.
-  (PR [#4137](https://github.com/pipecat-ai/pipecat/pull/4137))
-
-- `GrokLLMService` and `GrokRealtimeLLMService` now live in the
-  `pipecat.services.xai` module alongside `XAIHttpTTSService`, since all three
-  use the same xAI API. Update imports from `pipecat.services.grok.*` to
-  `pipecat.services.xai.*` (e.g. `from pipecat.services.xai.llm import
-  GrokLLMService`).
-  (PR [#4142](https://github.com/pipecat-ai/pipecat/pull/4142))
-
-- ⚠️ Bumped `mem0ai` dependency from `~=0.1.94` to `>=1.0.8,<2`. Users of the
-  `mem0` extra will need to update their mem0ai package.
-  (PR [#4156](https://github.com/pipecat-ai/pipecat/pull/4156))
-
-### Deprecated
-
-- `pipecat.services.grok.llm`, `pipecat.services.grok.realtime.llm`, and
-  `pipecat.services.grok.realtime.events` are deprecated. The old import paths
-  still work but emit a `DeprecationWarning`; use `pipecat.services.xai.llm`,
-  `pipecat.services.xai.realtime.llm`, and
-  `pipecat.services.xai.realtime.events` instead.
-  (PR [#4142](https://github.com/pipecat-ai/pipecat/pull/4142))
-
-### Removed
-
-- ⚠️ `TTSService.add_word_timestamps()` no longer supports the `"Reset"` and
-  `"TTSStoppedFrame"` sentinel strings. If you have a custom TTS service that
-  called `await self.add_word_timestamps([("Reset", 0)])` or `await
-  self.add_word_timestamps([("TTSStoppedFrame", 0), ("Reset", 0)], ctx_id)`,
-  replace them with `await self.append_to_audio_context(ctx_id,
-  TTSStoppedFrame(context_id=ctx_id))` and let `_handle_audio_context` manage
-  the word-timestamp reset automatically.
-  (PR [#4145](https://github.com/pipecat-ai/pipecat/pull/4145))
-
-- Removed `SambaNovaSTTService`. SambaNova no longer offers speech-to-text
-  audio models. Use another STT provider instead.
-  (PR [#4154](https://github.com/pipecat-ai/pipecat/pull/4154))
-
-### Fixed
-
-- Fixed Gemini Live (`GoogleGeminiLiveLLMService`) not honoring
-  `settings.system_instruction`. The system instruction was being read from a
-  deprecated constructor parameter instead of the settings object, causing it
-  to be silently ignored.
-  (PR [#4089](https://github.com/pipecat-ai/pipecat/pull/4089))
-
-- Fixed `AWSBedrockLLMAdapter` sending an empty message list to the API when
-  the only message in context was a system message. The lone system message is
-  now converted to "user" role instead of being extracted, matching the
-  existing Anthropic adapter behavior.
-  (PR [#4089](https://github.com/pipecat-ai/pipecat/pull/4089))
-
-- Fixed Gemini Live pipeline hanging indefinitely when an `EndFrame` was
-  deferred while waiting for the bot to finish responding and `turn_complete`
-  never arrived. As a possible root-cause fix, `turn_complete` messages are now
-  handled even if they lack `usage_metadata`. As a fallback, the deferred
-  `EndFrame` now has a 30-second safety timeout.
-  (PR [#4125](https://github.com/pipecat-ai/pipecat/pull/4125))
-
-- Fixed ElevenLabs WebSocket disconnections (1008 "Maximum simultaneous
-  contexts exceeded") caused by rapid user interruptions. When interruptions
-  arrived before any TTS text was generated, phantom contexts were created on
-  the ElevenLabs server that were never closed, eventually exceeding the
-  5-context limit.
-  (PR [#4126](https://github.com/pipecat-ai/pipecat/pull/4126))
-
-- Fixed the final sentence being dropped from the conversation context when
-  using RTVI text input with non-word-timestamp TTS services. The
-  `LLMFullResponseEndFrame` was racing ahead of the last `TTSTextFrame`,
-  causing the `LLMAssistantAggregator` to finalize the context before the final
-  sentence arrived.
-  (PR [#4127](https://github.com/pipecat-ai/pipecat/pull/4127))
-
-- Fixed audio crackling and popping in recordings when both user and bot are
-  speaking. `AudioBufferProcessor` no longer injects silence into a track's
-  buffer while that track is actively producing audio, preventing mid-utterance
-  interruptions in the recorded output.
-  (PR [#4135](https://github.com/pipecat-ai/pipecat/pull/4135))
-
-- Fixed websocket TTS word timestamps so interrupted contexts cannot leak stale
-  words or backward PTS values into later turns.
-  (PR [#4145](https://github.com/pipecat-ai/pipecat/pull/4145))
-
-- Fixed a race condition in `InterruptibleTTSService` where, if `run_tts` had
-  been invoked but `BotStartedSpeakingFrame` had not yet been received, a user
-  interruption could allow stale audio to leak through.
-  (PR [#4145](https://github.com/pipecat-ai/pipecat/pull/4145))
-
-- Fixed Gemini Live local VAD mode (`GeminiVADParams(disabled=True)` with
-  external VAD) not working. The bot now correctly detects user speech and
-  signals turn boundaries to the Gemini API.
-  (PR [#4146](https://github.com/pipecat-ai/pipecat/pull/4146))
-
-- Fixed Gemini Live message handling to process all `server_content` fields
-  independently. Gemini 3.x can bundle multiple fields (e.g. `model_turn` and
-  `output_transcription`) on the same message, but the previous `elif` chain
-  only processed the first match, silently dropping the rest.
-  (PR [#4147](https://github.com/pipecat-ai/pipecat/pull/4147))
-
-- Fixed `ServiceSwitcher` with `ServiceSwitcherStrategyFailover` incorrectly
-  triggering failover when `ErrorFrame`s from other pipeline stages (e.g. TTS)
-  propagated upstream through the switcher. Previously, any non-fatal error
-  passing through would be misattributed to the active service and trigger an
-  unwanted service switch. Now only errors originating from the switcher's own
-  managed services trigger failover.
-  (PR [#4149](https://github.com/pipecat-ai/pipecat/pull/4149))
-
-- Fixed `LiveKitOutputTransport` not clearing the `rtc.AudioSource` internal
-  buffer on interruption, causing the bot to continue speaking for several
-  seconds after being interrupted.
-  (PR [#4151](https://github.com/pipecat-ai/pipecat/pull/4151))
-
-- Fixed a crash in OpenAI LLM processing when the provider returns
-  `chunk.choices[0].delta.audio = None`, which caused `'NoneType' object has no
-  attribute 'get'` errors during audio transcript handling.
-  (PR [#4152](https://github.com/pipecat-ai/pipecat/pull/4152))
-
-- Fixed error floods in `DeepgramSTTService` when the WebSocket connection
-  drops. With Deepgram SDK 6.x, `send_media()` raises exceptions on a dead
-  connection instead of silently failing, causing every queued audio frame to
-  log an error. Now `send_media()` failures are caught gracefully — a single
-  warning is logged and audio frames are skipped until the existing
-  reconnection logic restores the connection.
-  (PR [#4153](https://github.com/pipecat-ai/pipecat/pull/4153))
-
-- `Mem0MemoryService` no longer blocks the event loop during memory storage and
-  retrieval. All Mem0 API calls now run in a background thread, and message
-  storage is fire-and-forget so it doesn't delay downstream processing.
-  (PR [#4156](https://github.com/pipecat-ai/pipecat/pull/4156))
-
-- Fixed `Mem0MemoryService` failing to store messages when the context
-  contained system or developer role messages. The Mem0 API only accepts user
-  and assistant roles, so other roles are now filtered out before storing.
-  (PR [#4156](https://github.com/pipecat-ai/pipecat/pull/4156))
-
-- Added missing `on_dtmf_event` callback to `LemonSliceTransportClient.setup()`
-  `DailyCallbacks` construction, fixing a `ValidationError` at pipeline setup
-  time.
-  (PR [#4161](https://github.com/pipecat-ai/pipecat/pull/4161))
-
-- Fixed an issue in `InworldTTSService` where, in cases of fast interruption,
-  we would continue receiving audio from the previous context.
-  (PR [#4167](https://github.com/pipecat-ai/pipecat/pull/4167))
-
-- Fixed a word timestamp interleaving issue in `InworldTTSService` when
-  processing multiple sentences.
-  (PR [#4167](https://github.com/pipecat-ai/pipecat/pull/4167))
-
-- Fixed duplicate `TTSStoppedFrame` being pushed in TTS services using
-  `push_stop_frames=True`. When the stop-frame timeout fired, a second
-  `TTSStoppedFrame` could be pushed after the normal one at context completion.
-  (PR [#4172](https://github.com/pipecat-ai/pipecat/pull/4172))
-
-- ⚠️ Fixed `DeepgramSTTService` compatibility with deepgram-sdk 6.1.0. The SDK
-  now requires explicit message objects for `send_keep_alive()`,
-  `send_close_stream()`, and `send_finalize()`. The minimum deepgram-sdk
-  version is now 6.1.0.
-  (PR [#4174](https://github.com/pipecat-ai/pipecat/pull/4174))
-
-- Fixed RTVI events not being delivered to clients when using WebSocket
-  transports. `ProtobufFrameSerializer` now sets `ignore_rtvi_messages=False`
-  by default.
-  (PR [#4176](https://github.com/pipecat-ai/pipecat/pull/4176))
-
-- Fixed a timing issue where turn detection timer tasks (idle controller,
-  speech timeout, turn analyzer, and turn completion) could miss their first
-  tick because the newly created asyncio task was not yet scheduled when the
-  caller continued.
-  (PR [#4183](https://github.com/pipecat-ai/pipecat/pull/4183))
-
-- Fixed `FastAPIWebsocketTransport` intermittently hanging on shutdown when the
-  remote side (e.g. Twilio) disconnects while audio is being sent. A race
-  condition between the send and receive paths could cause the
-  `on_client_disconnected` callback to be skipped, leaving the pipeline waiting
-  for a disconnect signal that never came.
-  (PR [#4186](https://github.com/pipecat-ai/pipecat/pull/4186))
-
-### Performance
-
-- `RimeTTSService` now handles Rime's `done` WebSocket message to complete
-  audio contexts immediately, eliminating the 3-second idle timeout that
-  previously added latency at the end of each utterance.
-  (PR [#4172](https://github.com/pipecat-ai/pipecat/pull/4172))
-
-## [0.0.107] - 2026-03-23
-
-### Added
-
-- Added `frame_order` parameter to `SyncParallelPipeline`. Set
-  `frame_order=FrameOrder.PIPELINE` to push synchronized output frames in
-  pipeline definition order (all frames from the first pipeline, then the
-  second, etc.) instead of the default arrival order.
-  (PR [#4029](https://github.com/pipecat-ai/pipecat/pull/4029))
-
-- Added `sync_with_audio` field to `OutputImageRawFrame`. When set to `True`,
-  the output transport queues image frames with audio so they are displayed
-  only after all preceding audio has been sent, enabling synchronized
-  audio/image playback.
-  (PR [#4029](https://github.com/pipecat-ai/pipecat/pull/4029))
-
-- Added `OpenAIResponsesLLMService`, a new LLM service that uses the OpenAI
-  Responses API. Supports streaming text, function calling, usage metrics, and
-  out-of-band inference. Works with the universal `LLMContext` and
-  `LLMContextAggregatorPair`. See
-  `examples/foundational/07-interruptible-openai-responses.py` and
-  `14-function-calling-openai-responses.py`.
-  (PR [#4074](https://github.com/pipecat-ai/pipecat/pull/4074))
-
-- Added `audio_out_auto_silence` parameter to `TransportParams` (defaults to
-  `True`). When set to `False`, the transport waits for audio data instead of
-  inserting silence when the output queue is empty, which is useful for
-  scenarios that require uninterrupted audio playback without artificial gaps.
-  (PR [#4104](https://github.com/pipecat-ai/pipecat/pull/4104))
-
-### Changed
-
-- Renamed tracing span attributes to align with OpenTelemetry GenAI semantic
-  conventions: `gen_ai.system` to `gen_ai.provider.name`, `system` to
-  `gen_ai.system_instructions`, `gen_ai.usage.cache_read_input_tokens` to
-  `gen_ai.usage.cache_read.input_tokens`, and
-  `gen_ai.usage.cache_creation_input_tokens` to
-  `gen_ai.usage.cache_creation.input_tokens`.
-  (PR [#3449](https://github.com/pipecat-ai/pipecat/pull/3449))
-
-- `DeepgramSageMakerTTSService` now correctly routes audio through the base
-  `TTSService` audio context queue. Audio frames are delivered via
-  `append_to_audio_context()` instead of being pushed directly, enabling proper
-  ordering, interruption handling, and start/stop frame lifecycle management.
-  Interruptions now trigger a `Clear` message to Deepgram (flushing its text
-  buffer) at the right time via `on_audio_context_interrupted`.
-  (PR [#4083](https://github.com/pipecat-ai/pipecat/pull/4083))
-
-- `GradiumTTSService` now sends a per-context `setup` message with
-  `client_req_id` before the first text message for each TTS context, following
-  Gradium's multiplexing protocol. Previously, a single setup message was sent
-  at connection time without a `client_req_id`, which prevented Gradium from
-  associating requests with their sessions when using `close_ws_on_eos=False`.
-  (PR [#4091](https://github.com/pipecat-ai/pipecat/pull/4091))
-
-### Fixed
-
-- Fixed stale `system_instruction` in LLM tracing spans by reading from
-  `_settings.system_instruction` instead of the removed `_system_instruction`
-  attribute.
-  (PR [#3449](https://github.com/pipecat-ai/pipecat/pull/3449))
-
-- Fixed `SyncParallelPipeline` breaking the Whisker debugger.
-  (PR [#4029](https://github.com/pipecat-ai/pipecat/pull/4029))
-
-- Fixed `SyncParallelPipeline` race condition where concurrent SystemFrame
-  processing (e.g. from RTVI) could corrupt sink queues and cause deadlocks.
-  SystemFrames now take a fast path that passes them through without draining
-  queued output.
-  (PR [#4029](https://github.com/pipecat-ai/pipecat/pull/4029))
-
-- Fixed TTS frame ordering so that non-system frames always arrive in correct
-  order relative to the `TTSStartedFrame`/`TTSAudioRawFrame`/`TTSStoppedFrame`
-  sequence. Previously these frames could race ahead of or behind audio context
-  frames, producing out-of-order output downstream.
-  (PR [#4075](https://github.com/pipecat-ai/pipecat/pull/4075))
-
-- Fixed `SarvamTTSService` audio and error frames now route through
-  `append_to_audio_context()` instead of `push_frame()`, ensuring correct
-  behavior with audio contexts and interruptions.
-  (PR [#4082](https://github.com/pipecat-ai/pipecat/pull/4082))
-
-- Fixed audio frame ordering and interruption handling in Fish Audio, LMNT,
-  Neuphonic, and Rime NonJson TTS services. These services were bypassing the
-  base `TTSService` audio context serialization queue by pushing audio frames
-  directly, which could cause out-of-order frames and broken interruptions
-  during speech.
-  (PR [#4090](https://github.com/pipecat-ai/pipecat/pull/4090))
-
-- Fixed Genesys AudioHook serializer to always include the `parameters` field in
-  protocol messages. The AudioHook protocol requires every message to carry a
-  `parameters` object (even if empty), but `_create_message` omitted it when no
-  parameters were provided. This caused clients that validate message structure
-  (including the Genesys reference implementation) to reject `pong` and
-  parameter-less `closed` responses, breaking server sequence tracking and
-  preventing `outputVariables` from reaching the Architect flow.
-  (PR [#4093](https://github.com/pipecat-ai/pipecat/pull/4093))
-
-## [0.0.106] - 2026-03-18
-
-### Added
-
-- Added optional `service` field to `ServiceUpdateSettingsFrame` (and its
-  subclasses `LLMUpdateSettingsFrame`, `TTSUpdateSettingsFrame`,
-  `STTUpdateSettingsFrame`) to target a specific service instance. When
-  `service` is set, only the matching service applies the settings; others
-  forward the frame unchanged. This enables updating a single service when
-  multiple services of the same type exist in the pipeline.
-  (PR [#4004](https://github.com/pipecat-ai/pipecat/pull/4004))
-
-- Added `sip_provider` and `room_geo` parameters to `configure()` in the Daily
-  runner. These convenience parameters let callers specify a SIP provider name
-  and geographic region directly without manually constructing
-  `DailyRoomProperties` and `DailyRoomSipParams`.
-  (PR [#4005](https://github.com/pipecat-ai/pipecat/pull/4005))
-
-- Added `PerplexityLLMAdapter` that automatically transforms conversation
-  messages to satisfy Perplexity's stricter API constraints (strict role
-  alternation, no non-initial system messages, last message must be user/tool).
-  Previously, certain conversation histories could cause Perplexity API errors
-  that didn't occur with OpenAI (`PerplexityLLMService` subclasses
-  `OpenAILLMService` since Perplexity uses an OpenAI-compatible API).
-  (PR [#4009](https://github.com/pipecat-ai/pipecat/pull/4009))
-
-- Added DTMF input event support to the Daily transport. Incoming DTMF tones
-  are now received via Daily's `on_dtmf_event` callback and pushed into the
-  pipeline as `InputDTMFFrame`, enabling bots to react to keypad presses from
-  phone callers.
-  (PR [#4047](https://github.com/pipecat-ai/pipecat/pull/4047))
-
-- Added `WakePhraseUserTurnStartStrategy` for triggering user turns based on
-  wake phrases, with support for `single_activation` mode. Deprecates
-  `WakeCheckFilter`.
-  (PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
-
-- Added `default_user_turn_start_strategies()` and
-  `default_user_turn_stop_strategies()` helper functions for composing custom
-  strategy lists.
-  (PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
-
-### Changed
-
-- Changed tool result JSON serialization to use `ensure_ascii=False`,
-  preserving UTF-8 characters instead of escaping them. This reduces context
-  size and token usage for non-English languages.
-  (PR [#3457](https://github.com/pipecat-ai/pipecat/pull/3457))
-
-- `OpenAIRealtimeSTTService`'s `noise_reduction` parameter is now part of
-  `OpenAIRealtimeSTTSettings`, making it runtime-updatable via
-  `STTUpdateSettingsFrame`. The direct `noise_reduction` init argument is
-  deprecated as of 0.0.106.
-  (PR [#3991](https://github.com/pipecat-ai/pipecat/pull/3991))
-
-- Updated `sarvamai` dependency from `0.1.26a2` (alpha) to `0.1.26` (stable
-  release).
-  (PR [#3997](https://github.com/pipecat-ai/pipecat/pull/3997))
-
-- `SimliVideoService` now extends `AIService` instead of `FrameProcessor`,
-  aligning it with the HeyGen and Tavus video services. It supports
-  `SimliVideoService.Settings(...)` for configuration and uses
-  `start()`/`stop()`/`cancel()` lifecycle methods. Existing constructor usage
-  (`api_key`, `face_id`, etc.) remains unchanged.
-  (PR [#4001](https://github.com/pipecat-ai/pipecat/pull/4001))
-
-- Update `pipecat-ai-small-webrtc-prebuilt` to `2.4.0`.
-  (PR [#4023](https://github.com/pipecat-ai/pipecat/pull/4023))
-
-- Nova Sonic assistant text transcripts are now delivered in real-time using
-  speculative text events instead of delayed final text events. Previously,
-  assistant text only arrived after all audio had finished playing, causing
-  laggy transcripts in client UIs. Speculative text arrives before each audio
-  chunk, providing text synchronized with what the bot is saying. This also
-  simplifies the internal text handling by removing the interruption re-push
-  hack and assistant text buffer.
-  (PR [#4042](https://github.com/pipecat-ai/pipecat/pull/4042))
-
-- Updated `daily-python` dependency to 0.25.0.
-  (PR [#4047](https://github.com/pipecat-ai/pipecat/pull/4047))
-
-- Added `enable_dialout` parameter to `configure()` in `pipecat.runner.daily`
-  to support dial-out rooms. Also narrowed misleading `Optional` type hints and
-  deduplicated token expiry calculation.
-  (PR [#4048](https://github.com/pipecat-ai/pipecat/pull/4048))
-
-- Extended `ProcessFrameResult` to stop strategies, allowing a stop strategy to
-  short-circuit evaluation of subsequent strategies by returning `STOP`.
-  (PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
-
-- `GradiumSTTService` now takes both an `encoding` and `sample_rate`
-  constructor argument which is assmebled in the class to form the
-  `input_format`. PCM accepts `8000`, `16000`, and `24000` Hz sample rates.
-  (PR [#4066](https://github.com/pipecat-ai/pipecat/pull/4066))
-
-- Improved `GradiumSTTService` transcription accuracy by reworking how text
-  fragments are accumulated and finalized. Previously, trailing words could be
-  dropped when the server's `flushed` response arrived before all text tokens
-  were delivered. The service now uses a short aggregation delay after flush to
-  capture trailing tokens, producing complete utterances.
-  (PR [#4066](https://github.com/pipecat-ai/pipecat/pull/4066))
-
-### Deprecated
-
-- `SimliVideoService.InputParams` is deprecated. Use the direct constructor
-  parameters `max_session_length`, `max_idle_time`, and `enable_logging`
-  instead.
-  (PR [#4001](https://github.com/pipecat-ai/pipecat/pull/4001))
-
-- Deprecated `LocalSmartTurnAnalyzerV2` and `LocalCoreMLSmartTurnAnalyzer`. Use
-  `LocalSmartTurnAnalyzerV3` instead. Instantiating these analyzers will now
-  emit a `DeprecationWarning`.
-  (PR [#4012](https://github.com/pipecat-ai/pipecat/pull/4012))
-
-- Deprecated `WakeCheckFilter` in favor of `WakePhraseUserTurnStartStrategy`.
-  (PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
-
-### Fixed
-
-- Fixed an issue where the default model for `OpenAILLMService` and
-  `AzureLLMService` was mistakenly reverted to `gpt-4o`. The defaults are now
-  restored to `gpt-4.1`.
-  (PR [#4000](https://github.com/pipecat-ai/pipecat/pull/4000))
-
-- Fixed a race condition where `EndTaskFrame` could cause the pipeline to shut
-  down before in-flight frames (e.g. LLM function call responses) finished
-  processing. `EndTaskFrame` and `StopTaskFrame` now flow through the pipeline
-  as `ControlFrame`s, ensuring all pending work is flushed before shutdown
-  begins. `CancelTaskFrame` and `InterruptionTaskFrame` remain immediate
-  (`SystemFrame`).
-  (PR [#4006](https://github.com/pipecat-ai/pipecat/pull/4006))
-
-- Fixed `ParallelPipeline` dropping or misordering frames during lifecycle
-  synchronization. Buffered frames are now flushed in the correct order
-  relative to synchronization frames (`StartFrame` goes first,
-  `EndFrame`/`CancelFrame` go after), and frames added to the buffer during
-  flush are also drained.
-  (PR [#4007](https://github.com/pipecat-ai/pipecat/pull/4007))
-
-- Fixed `TTSService` potentially canceling in-flight audio during shutdown. The
-  stop sequence now waits for all queued audio contexts to finish processing
-  before canceling the stop frame task.
-  (PR [#4007](https://github.com/pipecat-ai/pipecat/pull/4007))
-
-- Fixed `Language` enum values (e.g. `Language.ES`) not being converted to
-  service-specific codes when passed via
-  `settings=Service.Settings(language=Language.ES)` at init time. This caused
-  API errors (e.g. 400 from Rime) because the raw enum was sent instead of the
-  expected language code (e.g. `"spa"`). Runtime updates via
-  `UpdateSettingsFrame` were unaffected. The fix centralizes conversion in the
-  base `TTSService` and `STTService` classes so all services handle this
-  consistently.
-  (PR [#4024](https://github.com/pipecat-ai/pipecat/pull/4024))
-
-- Fixed `DeepgramSTTService` ignoring the `base_url` scheme when using `ws://`
-  or `http://`. Previously these were silently overwritten with `wss://` /
-  `https://`, breaking air-gapped or private deployments that don't use TLS.
-  All scheme choices (`wss://`, `https://`, `ws://`, `http://`, or bare
-  hostname) are now respected.
-  (PR [#4026](https://github.com/pipecat-ai/pipecat/pull/4026))
-
-- Fixed `LLMSwitcher.register_function()` and `register_direct_function()` not
-  accepting or forwarding the `timeout_secs` parameter.
-  (PR [#4037](https://github.com/pipecat-ai/pipecat/pull/4037))
-
-- Fixed empty user transcriptions in Nova Sonic causing spurious interruptions.
-  Previously, an empty transcription could trigger an interruption of the
-  assistant's response even though the user hadn't actually spoken.
-  (PR [#4042](https://github.com/pipecat-ai/pipecat/pull/4042))
-
-- Fixed `SonioxSTTService` and `OpenAIRealtimeSTTService` crash when language
-  parameters contain plain strings instead of `Language` enum values.
-  (PR [#4046](https://github.com/pipecat-ai/pipecat/pull/4046))
-
-- Fixed premature user turn stops caused by late transcriptions arriving
-  between turns. A stale transcript from the previous turn could persist into
-  the next turn and trigger a stop before the current turn's real transcript
-  arrived. Stop strategies are now reset at both turn start and turn stop to
-  prevent state from leaking across turn boundaries.
-  (PR [#4057](https://github.com/pipecat-ai/pipecat/pull/4057))
-
-- Fixed raw language strings like `"de-DE"` silently failing when passed to
-  TTS/STT services (e.g. ElevenLabs producing no audio). Raw strings now go
-  through the same `Language` enum resolution as enum values, so regional codes
-  like `"de-DE"` are properly converted to service-expected formats like
-  `"de"`. Unrecognized strings log a warning instead of failing silently.
-  (PR [#4058](https://github.com/pipecat-ai/pipecat/pull/4058))
-
-- Fixed Deepgram STT list-type settings (`keyterm`, `keywords`, `search`,
-  `redact`, `replace`) being stringified instead of passed as lists to the SDK,
-  which caused them to be sent as literal strings (e.g. `"['pipecat']"`) in the
-  WebSocket query params.
-  (PR [#4063](https://github.com/pipecat-ai/pipecat/pull/4063))
-
-- Fixed `MinWordsUserTurnStartStrategy` including text below the word threshold
-  in the output by resetting aggregation when the minimum word count is not
-  met.
-  (PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
-
-- Fixed audio overlap and potential dropped TTS content when multiple assistant
-  turns occur in quick succession. `TTSService` now flushes remaining text
-  before pausing frame processing on `LLMFullResponseEndFrame`/`EndFrame`,
-  instead of pausing first.
-  (PR [#4071](https://github.com/pipecat-ai/pipecat/pull/4071))
-
-### Security
-
-- Bumped PyJWT minimum version from 2.10.1 to 2.12.0 in the `livekit` extra to
-  address CVE-2026-32597 (GHSA-752w-5fwx-jx9f), where PyJWT <= 2.11.0 accepted
-  unknown `crit` header extensions.
-  (PR [#4035](https://github.com/pipecat-ai/pipecat/pull/4035))
-
-## [0.0.105] - 2026-03-10
-
-### Added
-
-- Added concurrent audio context support: `CartesiaTTSService` can now
-  synthesize the next sentence while the previous one is still playing, by
-  setting `pause_frame_processing=False` and routing each sentence through its
-  own audio context queue.
-  (PR [#3804](https://github.com/pipecat-ai/pipecat/pull/3804))
-
-- Added custom video track support to Daily transport. Use
-  `video_out_destinations` in `DailyParams` to publish multiple video tracks
-  simultaneously, mirroring the existing `audio_out_destinations` feature.
-  (PR [#3831](https://github.com/pipecat-ai/pipecat/pull/3831))
-
-- Added `ServiceSwitcherStrategyFailover` that automatically switches to the
-  next service when the active service reports a non-fatal error. Recovery
-  policies can be implemented via the `on_service_switched` event handler.
-  (PR [#3861](https://github.com/pipecat-ai/pipecat/pull/3861))
-
-- Added optional `timeout_secs` parameter to `register_function()` and
-  `register_direct_function()` for per-tool function call timeout control,
-  overriding the global `function_call_timeout_secs` default.
-  (PR [#3915](https://github.com/pipecat-ai/pipecat/pull/3915))
-
-- Added `cloud-audio-only` recording option to Daily transport's
-  `enable_recording` property.
-  (PR [#3916](https://github.com/pipecat-ai/pipecat/pull/3916))
-
-- Wired up `system_instruction` in `BaseOpenAILLMService`,
-  `AnthropicLLMService`, and `AWSBedrockLLMService` so it works as a default
-  system prompt, matching the behavior of the Google services. This enables
-  sharing a single `LLMContext` across multiple LLM services, where each
-  service provides its own system instruction independently.
-
-    ```python
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        system_instruction="You are a helpful assistant.",
-    )
-
-    context = LLMContext()
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        context.add_message({"role": "user", "content": "Please introduce yourself."})
-        await task.queue_frames([LLMRunFrame()])
-    ```
-  (PR [#3918](https://github.com/pipecat-ai/pipecat/pull/3918))
-
-- Added `vad_threshold` parameter to `AssemblyAIConnectionParams` for
-  configuring voice activity detection sensitivity in U3 Pro. Aligning this
-  with external VAD thresholds (e.g., Silero VAD) prevents the "dead zone"
-  where AssemblyAI transcribes speech that VAD hasn't detected yet.
-  (PR [#3927](https://github.com/pipecat-ai/pipecat/pull/3927))
-
-- Added `push_empty_transcripts` parameter to `BaseWhisperSTTService` and
-  `OpenAISTTService` to allow empty transcripts to be pushed downstream as
-  `TranscriptionFrame` instead of discarding them (the default behavior). This
-  is intended for situations where VAD fires even though the user did not
-  speak. In these cases, it is useful to know that nothing was transcribed so
-  that the agent can resume speaking, instead of waiting longer for a
-  transcription.
-  (PR [#3930](https://github.com/pipecat-ai/pipecat/pull/3930))
-
-- LLM services (`BaseOpenAILLMService`, `AnthropicLLMService`,
-  `AWSBedrockLLMService`) now log a warning when both `system_instruction` and
-  a system message in the context are set. The constructor's
-  `system_instruction` takes precedence.
-  (PR [#3932](https://github.com/pipecat-ai/pipecat/pull/3932))
-
-- Runtime settings updates (via `STTUpdateSettingsFrame`) now work for AWS
-  Transcribe, Azure, Cartesia, Deepgram, ElevenLabs Realtime, Gradium, and
-  Soniox STT services. Previously, changing settings at runtime only stored the
-  new values without reconnecting.
-  (PR [#3946](https://github.com/pipecat-ai/pipecat/pull/3946))
-
-- Exposed `on_summary_applied` event on `LLMAssistantAggregator`, allowing
-  users to listen for context summarization events without accessing private
-  members.
-  (PR [#3947](https://github.com/pipecat-ai/pipecat/pull/3947))
-
-- Deepgram Flux STT settings (`keyterm`, `eot_threshold`,
-  `eager_eot_threshold`, `eot_timeout_ms`) can now be updated mid-stream via
-  `STTUpdateSettingsFrame` without triggering a reconnect. The new values are
-  sent to Deepgram as a Configure WebSocket message on the existing connection.
-  (PR [#3953](https://github.com/pipecat-ai/pipecat/pull/3953))
-
-- Added `system_instruction` parameter to `run_inference` across all LLM
-  services, allowing callers to override the system prompt for one-shot
-  inference calls. Used by `_generate_summary` to pass the summarization prompt
-  cleanly.
-  (PR [#3968](https://github.com/pipecat-ai/pipecat/pull/3968))
-
-### Changed
-
-- Audio context management (previously in `AudioContextTTSService`) is now
-  built into `TTSService`. All WebSocket providers (`cartesia`, `elevenlabs`,
-  `asyncai`, `inworld`, `rime`, `gradium`, `resembleai`) now inherit from
-  `WebsocketTTSService` directly. Word-timestamp baseline is set automatically
-  on the first audio chunk of each context instead of requiring each provider
-  to call `start_word_timestamps()` in their receive loop.
-  (PR [#3804](https://github.com/pipecat-ai/pipecat/pull/3804))
-
-- Daily transport now uses `CustomVideoSource`/`CustomVideoTrack` instead of
-  `VirtualCameraDevice` for the default camera output, mirroring how audio
-  already works with `CustomAudioSource`/`CustomAudioTrack`.
-  (PR [#3831](https://github.com/pipecat-ai/pipecat/pull/3831))
-
-- ⚠️ Updated `DeepgramSTTService` to use `deepgram-sdk` v6. The `LiveOptions`
-  class was removed from the SDK and is now provided by pipecat directly;
-  import it from `pipecat.services.deepgram.stt` instead of `deepgram`.
-  (PR [#3848](https://github.com/pipecat-ai/pipecat/pull/3848))
-
-- `ServiceSwitcherStrategy` base class now provides a `handle_error()` hook for
-  subclasses to implement error-based switching. `ServiceSwitcher` defaults to
-  `ServiceSwitcherStrategyManual` and `strategy_type` is now optional.
-  (PR [#3861](https://github.com/pipecat-ai/pipecat/pull/3861))
-
-- Support for Voice Focus 2.0 models.
-    - Updated `aic-sdk` to `~=2.1.0` to support Voice Focus 2.0 models.
-    - Cleaned unused `ParameterFixedError` exception handling in `AICFilter`
-      parameter setup.
-  (PR [#3889](https://github.com/pipecat-ai/pipecat/pull/3889))
-
-- `max_context_tokens` and `max_unsummarized_messages` in
-  `LLMAutoContextSummarizationConfig` (and deprecated
-  `LLMContextSummarizationConfig`) can now be set to `None` independently to
-  disable that summarization threshold. At least one must remain set.
-  (PR [#3914](https://github.com/pipecat-ai/pipecat/pull/3914))
-
-- ⚠️ Removed `formatted_finals` and `word_finalization_max_wait_time` from
-  `AssemblyAIConnectionParams` as these were v2 API parameters not supported in
-  v3. Clarified that `format_turns` only applies to Universal-Streaming models;
-  U3 Pro has automatic formatting built-in.
-  (PR [#3927](https://github.com/pipecat-ai/pipecat/pull/3927))
-
-- Changed `DeepgramTTSService` to send a Clear message on interruption instead
-  of disconnecting and reconnecting the WebSocket, allowing the connection to
-  persist throughout the session.
-  (PR [#3958](https://github.com/pipecat-ai/pipecat/pull/3958))
-
-- Re-added `enhancement_level` support to `AICFilter` with runtime
-  `FilterEnableFrame` control, applying `ProcessorParameter.Bypass` and
-  `ProcessorParameter.EnhancementLevel` together.
-  (PR [#3961](https://github.com/pipecat-ai/pipecat/pull/3961))
-
-- Updated `daily-python` dependency from `~=0.23.0` to `~=0.24.0`.
-  (PR [#3970](https://github.com/pipecat-ai/pipecat/pull/3970))
-
-- Updated `FishAudioTTSService` default model from `s1` to `s2-pro`, matching
-  Fish Audio's latest recommended model for improved quality and speed.
-  (PR [#3973](https://github.com/pipecat-ai/pipecat/pull/3973))
-
-- `AzureSTTService` `region` parameter is now optional when `private_endpoint`
-  is provided. A `ValueError` is raised if neither is given, and a warning is
-  logged if both are provided (`private_endpoint` takes priority).
-  (PR [#3974](https://github.com/pipecat-ai/pipecat/pull/3974))
-
-### Deprecated
-
-- Deprecated `AudioContextTTSService` and `AudioContextWordTTSService`.
-  Subclass `WebsocketTTSService` directly instead; audio context management is
-  now part of the base `TTSService`.
-  - Deprecated `WordTTSService`, `WebsocketWordTTSService`, and
-    `InterruptibleWordTTSService`. Word timestamp logic is now always active in
-    `TTSService` and no longer needs to be opted into via a subclass.
-  (PR [#3804](https://github.com/pipecat-ai/pipecat/pull/3804))
-
-- Deprecated `pipecat.services.google.llm_vertex`,
-  `pipecat.services.google.llm_openai`, and
-  `pipecat.services.google.gemini_live.llm_vertex` modules. Use
-  `pipecat.services.google.vertex.llm`, `pipecat.services.google.openai.llm`,
-  and `pipecat.services.google.gemini_live.vertex.llm` instead. The old import
-  paths still work but will emit a `DeprecationWarning`.
-  (PR [#3980](https://github.com/pipecat-ai/pipecat/pull/3980))
-
-### Removed
-
-- ⚠️ Removed `supports_word_timestamps` parameter from `TTSService.__init__()`.
-  Word timestamp logic is now always active. Remove this argument from any
-  custom subclass `super().__init__()` calls.
-  (PR [#3804](https://github.com/pipecat-ai/pipecat/pull/3804))
-
-### Fixed
-
-- Fixed `DeepgramSTTService` keepalive ping timeout disconnections. The
-  deepgram-sdk v6 removed automatic keepalive; pipecat now sends explicit
-  `KeepAlive` messages every 5 seconds, within the recommended 3–5 second
-  interval before Deepgram's 10-second inactivity timeout.
-  (PR [#3848](https://github.com/pipecat-ai/pipecat/pull/3848))
-
-- Fixed `BufferError: Existing exports of data: object cannot be re-sized` in
-  `AICFilter` caused by holding a `memoryview` on the mutable audio buffer
-  across async yield points.
-  (PR [#3889](https://github.com/pipecat-ai/pipecat/pull/3889))
-
-- Fixed TTS context not being appended to the assistant message history when
-  using `TTSSpeakFrame` with `append_to_context=True` with some TTS providers.
-  (PR [#3936](https://github.com/pipecat-ai/pipecat/pull/3936))
-
-- Fixed context summarization leaving orphaned tool responses in the kept
-  context when tool calls were moved to the summarized portion.
-  (PR [#3937](https://github.com/pipecat-ai/pipecat/pull/3937))
-
-- Fixed turn completion state not resetting at end of LLM responses.
-  `LLMFullResponseEndFrame` is pushed (not received) by the LLM service, so the
-  mixin now handles it in `push_frame` instead of `process_frame`.
-  (PR [#3956](https://github.com/pipecat-ai/pipecat/pull/3956))
-
-- Fixed turn completion instructions being injected as a context system message
-  instead of using `system_instruction`. This caused warning spam when
-  `system_instruction` was also set and didn't persist across full context
-  updates.
-  (PR [#3957](https://github.com/pipecat-ai/pipecat/pull/3957))
-
-- Fixed `TTSService` audio context queue getting blocked when
-  `append_to_audio_context()` was called with a `None` context ID, which
-  prevented subsequent audio from being delivered.
-  (PR [#3958](https://github.com/pipecat-ai/pipecat/pull/3958))
-
-- Fixed `on_call_state_updated` event handler in LiveKit transport receiving
-  incorrect number of arguments due to redundant `self` passed to
-  `_call_event_handler`.
-  (PR [#3959](https://github.com/pipecat-ai/pipecat/pull/3959))
-
-- Fixed OpenAI Realtime, OpenAI Realtime Beta, and Grok realtime services
-  treating `conversation_already_has_active_response` as a fatal error. These
-  services now log it as a non-fatal debug event when a response is already in
-  progress.
-  (PR [#3960](https://github.com/pipecat-ai/pipecat/pull/3960))
-
-- Fixed `SmallWebRTCConnection` silently discarding messages sent before the
-  data channel is open by queuing them and flushing once the channel is ready.
-  A bounded queue (`MAX_MESSAGE_QUEUE_SIZE = 50`) prevents unbounded memory
-  growth, and a 10-second timeout after connection clears the queue and falls
-  back to discard mode if the data channel never opens.
-  (PR [#3962](https://github.com/pipecat-ai/pipecat/pull/3962))
-
-- Fixed `AzureSTTService` failing to initialize when `private_endpoint` is
-  provided. The Azure Speech SDK's `SpeechConfig` does not accept both `region`
-  and `endpoint` simultaneously, so they are now passed conditionally.
-  (PR [#3967](https://github.com/pipecat-ai/pipecat/pull/3967))
-
-- Fixed `GoogleLLMService` ignoring the `system_instruction` set via
-  constructor or `GoogleLLMSettings` when a system message was also present in
-  the context. The settings value now correctly takes priority, and a warning
-  is logged when both are set.
-  (PR [#3976](https://github.com/pipecat-ai/pipecat/pull/3976))
-
-### Other
-
-- Updated foundational examples to use `system_instruction` on LLM services
-  instead of adding system messages to `LLMContext`.
-  (PR [#3918](https://github.com/pipecat-ai/pipecat/pull/3918))
-
-- Updated AssemblyAI turn detection example to use `keyterms_prompt` list
-  format instead of `prompt` string for improved clarity.
-  (PR [#3929](https://github.com/pipecat-ai/pipecat/pull/3929))
-
-- Updated foundational examples and eval scripts to use `"user"` role instead
-  of `"system"` when adding messages to `LLMContext`, since system prompts
-  should be set via `system_instruction` on the LLM service.
-  (PR [#3931](https://github.com/pipecat-ai/pipecat/pull/3931))
-
 ## [0.0.104] - 2026-03-02
 
 ### Added

COMMUNITY_INTEGRATIONS.md

@@ -65,25 +65,12 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
 
 #### Websocket-based Services
 
-**Base class:** `WebsocketSTTService`
-
-**Use for:** Services where you manage the websocket connection directly. Combines `STTService` with `WebsocketService` for automatic reconnection and keepalive support.
-
-**Examples:**
-
-- [CartesiaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/stt.py)
-- [ElevenLabsRealtimeSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/stt.py)
-
-#### SDK-based Streaming Services
-
 **Base class:** `STTService`
 
-**Use for:** Streaming services where the provider's Python SDK manages the connection internally.
-
 **Examples:**
 
 - [DeepgramSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/deepgram/stt.py)
-- [GoogleSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/stt.py)
+- [SpeechmaticsSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/speechmatics/stt.py)
 
 #### File-based Services
 
@@ -121,59 +108,55 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
 
 #### Key requirements:
 
-- **`_process_context(self, context: LLMContext)`** — The main method that processes an LLM context and generates a response. Each LLM service overrides `process_frame` to extract context from `LLMContextFrame` and calls `_process_context`.
-
-- **`adapter_class`** — Class attribute pointing to a `BaseLLMAdapter` subclass. Defaults to `OpenAILLMAdapter`. Non-OpenAI services must implement their own adapter (see `src/pipecat/adapters/base_llm_adapter.py`) with methods:
-  - `get_llm_invocation_params(context)` — Extract provider-specific params from universal context
-  - `to_provider_tools_format(tools_schema)` — Convert standard tools to provider format
-  - `get_messages_for_logging(context)` — Format messages for logging
-  - Reference adapters: `src/pipecat/adapters/services/` (anthropic, gemini, bedrock, etc.)
-
 - **Frame sequence:** Output must follow this frame sequence pattern:
-  - `LLMFullResponseStartFrame` — Signals the start of an LLM response
-  - `LLMTextFrame` — Contains LLM content, typically streamed as tokens
-  - `LLMFullResponseEndFrame` — Signals the end of an LLM response
+  - `LLMFullResponseStartFrame` - Signals the start of an LLM response
+  - `LLMTextFrame` - Contains LLM content, typically streamed as tokens
+  - `LLMFullResponseEndFrame` - Signals the end of an LLM response
 
-- **Thought frames (reasoning models):** If the model supports extended thinking / chain-of-thought, emit thought frames alongside the response:
-  - `LLMThoughtStartFrame` — Signals the start of a thought
-  - `LLMThoughtTextFrame` — Contains thought content, streamed as tokens
-  - `LLMThoughtEndFrame` — Signals the end of a thought
-
-- **Context aggregation** is handled by the framework via `LLMContext` + `LLMContextAggregatorPair`. The LLM service just processes context it receives — no need to implement aggregators.
+- **Context aggregation:** Implement context aggregation to collect user and assistant content:
+  - Aggregators come in pairs with a `user()` instance and `assistant()` instance
+  - Context must adhere to the `LLMContext` universal format
+  - Aggregators should handle adding messages, function calls, and images to the context
 
 ### TTS (Text-to-Speech) Services
 
-#### WebsocketTTSService
+#### AudioContextWordTTSService
 
-**Use for:** Websocket-based streaming services (with or without word timestamps)
+**Use for:** Websocket-based services supporting word/timestamp alignment
 
-**Examples:**
+**Example:**
 
 - [CartesiaTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/tts.py)
-- [ElevenLabsTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
 
 #### InterruptibleTTSService
 
-**Use for:** Websocket-based services without word timestamps that reconnect on interruption (e.g. don't support a context ID or interruption message)
+**Use for:** Websocket-based services without word/timestamp alignment, requiring disconnection on interruption
 
 **Example:**
 
 - [SarvamTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/sarvam/tts.py)
 
+#### WordTTSService
+
+**Use for:** HTTP-based services supporting word/timestamp alignment
+
+**Example:**
+
+- [ElevenLabsHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
+
 #### TTSService
 
-**Use for:** HTTP-based services (word timestamps are supported in the base class)
+**Use for:** HTTP-based services without word/timestamp alignment
 
-**Examples:**
+**Example:**
 
 - [GoogleHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/tts.py)
-- [OpenAITTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/openai/tts.py)
 
 #### Key requirements:
 
-- For websocket services, use asyncio WebSocket implementation
+- For websocket services, use asyncio WebSocket implementation (required for v13+ support)
 - Handle idle service timeouts with keepalives
-- TTS services push both audio (`TTSAudioRawFrame`) and text (`TTSTextFrame`) frames
+- TTSServices push both audio (`TTSRawAudioFrame`) and text (`TTSTextFrame`) frames
 
 ### Telephony Serializers
 
@@ -217,9 +200,9 @@ Vision services process images and provide analysis such as descriptions, object
 
 #### Key requirements:
 
-- Must implement `run_vision` method that takes a `UserImageRawFrame` and returns an `AsyncGenerator[Frame, None]`
-- The method processes the image frame and yields frames with analysis results
-- Must yield the frame sequence: `VisionFullResponseStartFrame`, `VisionTextFrame`, `VisionFullResponseEndFrame`
+- Must implement `run_vision` method that takes an `LLMContext` and returns an `AsyncGenerator[Frame, None]`
+- The method processes the latest image in the context and yields frames with analysis results
+- Typically yields `TextFrame` objects containing descriptions or answers
 
 ## Implementation Guidelines
 
@@ -248,105 +231,49 @@ def can_generate_metrics(self) -> bool:
     return True
 ```
 
-### Service Settings
+### Dynamic Settings Updates
 
-Every AI service (STT, LLM, TTS, image generation, etc.) exposes a **Settings dataclass** that serves two roles:
+STT, LLM, and TTS services support runtime configuration changes via `*UpdateSettingsFrame`s (e.g. `STTUpdateSettingsFrame`, `TTSUpdateSettingsFrame`, `LLMUpdateSettingsFrame`).
 
-1. **Store mode** — the service's `self._settings` holds the current value of every runtime-updatable field.
-2. **Delta mode** — an update frame (e.g. `TTSUpdateSettingsFrame`) specifies only the fields that should change; unspecified fields remain `NOT_GIVEN`.
-
-#### Defining your Settings class
-
-Extend `STTSettings`, `TTSSettings`, `LLMSettings`, or `ImageGenSettings` (or, if your service directly subclasses `AIService`, `ServiceSettings`). 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**:
+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:
 
 ```python
 from dataclasses import dataclass, field
 
-from pipecat.services.settings import TTSSettings, NOT_GIVEN
+from pipecat.services.settings import STTSettings, NOT_GIVEN
 
 @dataclass
-class MyTTSSettings(TTSSettings):
-    """Settings for MyTTS service.
+class MySTTSettings(STTSettings):
+    """Settings for my STT service.
 
     Parameters:
-        speaking_rate: Speed multiplier (0.5–2.0).
+        region: Cloud region for the service.
     """
 
-    speaking_rate: float | None = field(default_factory=lambda: NOT_GIVEN)
+    region: str = field(default_factory=lambda: NOT_GIVEN)
 ```
 
-**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`.
-
-Add a `Settings` **class attribute** that points to your settings dataclass. This lets callers access the settings class through the service itself (e.g. `MyTTSService.Settings(...)`) without a separate import:
+The service stores its current settings in `self._settings` and declares the type with a class-level annotation for editor support:
 
 ```python
-from typing import Optional
+class MySTTService(STTService):
+    _settings: MySTTSettings
 
-class MyTTSService(TTSService):
-    Settings = MyTTSSettings
-    _settings: Settings
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        settings: Optional[Settings] = None,
-        **kwargs,
-    ):
-        # 1. Defaults — every field has a real value (store mode).
-        default_settings = self.Settings(
-            model="my-model-v1",
-            voice="default-voice",
-            language="en",
-            speaking_rate=1.0,
+    def __init__(self, *, model: str, language: str, region: str, **kwargs):
+        # An initial value should be provided for every settings field.
+        # This will be validated at service start.
+        # (If you track sample_rate, it can be a placeholder value like 0; see
+        # "Sample Rate Handling").
+        super().__init__(
+            settings=MySTTSettings(model=model, language=language, region=region), **kwargs
         )
-
-        # 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 — access Settings through the service class
-svc = MyTTSService(
-    api_key="sk-xxx",
-    settings=MyTTSService.Settings(voice="custom-voice"),
-)
-```
-
-#### Reacting to runtime changes
-
-AI 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: TTSSettings) -> dict[str, Any]:
-    """Apply a settings update, reconfiguring the connection if needed."""
+async def _update_settings(self, update: STTSettings) -> dict[str, Any]:
+    """Apply a settings update, reconfiguring the recognizer if needed."""
     changed = await super()._update_settings(update)
 
     if not changed:
@@ -365,7 +292,7 @@ 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: TTSSettings) -> dict[str, Any]:
+async def _update_settings(self, update: STTSettings) -> dict[str, Any]:
     changed = await super()._update_settings(update)
 
     if not changed:
@@ -398,7 +325,7 @@ Note that `self.sample_rate` is a `@property` set in the TTSService base class,
 
 Use Pipecat's tracing decorators:
 
-- **STT:** `@traced_stt` - decorate `_handle_transcription(self, transcript, is_final, language)` (the standard method name convention)
+- **STT:** `@traced_stt` - decorate a function that handles `transcript`, `is_final`, `language` as args
 - **LLM:** `@traced_llm` - decorate the `_process_context()` method
 - **TTS:** `@traced_tts` - decorate the `run_tts()` method
 
@@ -420,15 +347,17 @@ For REST-based communication, use aiohttp. Pipecat includes this as a required d
 - Wrap API calls in appropriate try/catch blocks
 - Handle rate limits and network failures gracefully
 - Provide meaningful error messages
-- When errors occur, raise exceptions AND push errors to notify the pipeline:
+- When errors occur, raise exceptions AND push `ErrorFrame`s to notify the pipeline:
 
 ```python
+from pipecat.frames.frames import ErrorFrame
+
 try:
     # Your API call
     result = await self._make_api_call()
 except Exception as e:
-    # Push error upstream to notify the pipeline
-    await self.push_error(f"{self} error: {e}", exception=e)
+    # Push error frame to pipeline
+    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
     # Raise or handle as appropriate
     raise
 ```

README.md

@@ -65,10 +65,6 @@ claude plugin marketplace add pipecat-ai/skills
 
 and install any of the available plugins.
 
-### 🧩 Community Integrations
-
-Build and share your own Pipecat service integrations! Browse existing [community integrations](https://docs.pipecat.ai/server/services/community-integrations) or check out our [guide](COMMUNITY_INTEGRATIONS.md) to create your own.
-
 ### 📺️ Pipecat TV Channel
 
 Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
@@ -85,20 +81,19 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
 
 ## 🧩 Available services
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [Novita](https://docs.pipecat.ai/server/services/llm/novita), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nvidia), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/llm/sarvam), [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                                                                                                                                                                                                                                                                                         |
-| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [Resemble](https://docs.pipecat.ai/server/services/tts/resemble), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Smallest](https://docs.pipecat.ai/server/services/tts/smallest), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [xAI](https://docs.pipecat.ai/server/services/tts/xai), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
-| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [Grok Voice Agent](https://docs.pipecat.ai/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/server/services/s2s/ultravox),                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
-| Serializers         | [Exotel](https://docs.pipecat.ai/server/utilities/serializers/exotel), [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx), [Vonage](https://docs.pipecat.ai/server/utilities/serializers/vonage)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
-| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [LemonSlice](https://docs.pipecat.ai/server/services/video/lemonslice), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
-| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/google-imagen), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
-| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
-| Community           | [Browse community integrations →](https://docs.pipecat.ai/server/services/community-integrations)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [Hathora](https://docs.pipecat.ai/server/services/stt/hathora), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                             |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hathora](https://docs.pipecat.ai/server/services/tts/hathora), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [Resemble](https://docs.pipecat.ai/server/services/tts/resemble), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
+| Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [Grok Voice Agent](https://docs.pipecat.ai/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/server/services/s2s/ultravox),                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| Serializers         | [Exotel](https://docs.pipecat.ai/server/utilities/serializers/exotel), [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx), [Vonage](https://docs.pipecat.ai/server/utilities/serializers/vonage)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+| Video               | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [LemonSlice](https://docs.pipecat.ai/server/services/video/lemonslice), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+| Memory              | [mem0](https://docs.pipecat.ai/server/services/memory/mem0)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+| Vision & Image      | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/google-imagen), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
 
 📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
 

changelog/3848.changed.md

@@ -0,0 +1 @@
+- ⚠️ Updated `DeepgramSTTService` to use `deepgram-sdk` v6. The `LiveOptions` class was removed from the SDK and is now provided by pipecat directly; import it from `pipecat.services.deepgram.stt` instead of `deepgram`.

changelog/3848.fixed.md

@@ -0,0 +1 @@
+- Fixed `DeepgramSTTService` keepalive ping timeout disconnections. The deepgram-sdk v6 removed automatic keepalive; pipecat now sends explicit `KeepAlive` messages every 5 seconds, within the recommended 3–5 second interval before Deepgram's 10-second inactivity timeout.

changelog/3889.changed.md

@@ -0,0 +1,3 @@
+- Support for Voice Focus 2.0 models.
+  - Updated `aic-sdk` to `~=2.1.0` to support Voice Focus 2.0 models.
+  - Cleaned unused `ParameterFixedError` exception handling in `AICFilter` parameter setup. 

changelog/3889.fixed.md

@@ -0,0 +1 @@
+- Fixed `BufferError: Existing exports of data: object cannot be re-sized` in `AICFilter` caused by holding a `memoryview` on the mutable audio buffer across async yield points.

changelog/3914.changed.md

@@ -0,0 +1 @@
+- `max_context_tokens` and `max_unsummarized_messages` in `LLMAutoContextSummarizationConfig` (and deprecated `LLMContextSummarizationConfig`) can now be set to `None` independently to disable that summarization threshold. At least one must remain set.

changelog/3915.added.md

@@ -0,0 +1 @@
+- Added optional `timeout_secs` parameter to `register_function()` and `register_direct_function()` for per-tool function call timeout control, overriding the global `function_call_timeout_secs` default.

changelog/3916.added.md

@@ -0,0 +1 @@
+- Added `cloud-audio-only` recording option to Daily transport's `enable_recording` property.

changelog/3918.added.md

@@ -0,0 +1,15 @@
+- Wired up `system_instruction` in `BaseOpenAILLMService`, `AnthropicLLMService`, and `AWSBedrockLLMService` so it works as a default system prompt, matching the behavior of the Google services. This enables sharing a single `LLMContext` across multiple LLM services, where each service provides its own system instruction independently.
+
+  ```python
+  llm = OpenAILLMService(
+      api_key=os.getenv("OPENAI_API_KEY"),
+      system_instruction="You are a helpful assistant.",
+  )
+
+  context = LLMContext()
+
+  @transport.event_handler("on_client_connected")
+  async def on_client_connected(transport, client):
+      context.add_message({"role": "user", "content": "Please introduce yourself."})
+      await task.queue_frames([LLMRunFrame()])
+  ```

changelog/3918.other.md

@@ -0,0 +1 @@
+- Updated foundational examples to use `system_instruction` on LLM services instead of adding system messages to `LLMContext`.

env.example

@@ -80,9 +80,15 @@ GOOGLE_TEST_CREDENTIALS=...
 # Gradium
 GRAPDIUM_API_KEY=...
 
+# Grok
+GROK_API_KEY=...
+
 # Groq
 GROQ_API_KEY=...
 
+# Hathora
+HATHORA_API_KEY=...
+
 # Heygen
 HEYGEN_API_KEY=...
 HEYGEN_LIVE_AVATAR_API_KEY=...
@@ -124,9 +130,6 @@ MISTRAL_API_KEY=...
 # Neuphonic
 NEUPHONIC_API_KEY=...
 
-# Novita
-NOVITA_API_KEY=...
-
 # NVIDIA
 NVIDIA_API_KEY=...
 
@@ -176,9 +179,6 @@ SENTRY_DSN=...
 SIMLI_API_KEY=...
 SIMLI_FACE_ID=...
 
-# Smallest
-SMALLEST_API_KEY=...
-
 # Smart turn
 LOCAL_SMART_TURN_MODEL_PATH=...
 FAL_SMART_TURN_API_KEY=...
@@ -212,6 +212,3 @@ WHATSAPP_TOKEN=...
 WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
 WHATSAPP_PHONE_NUMBER_ID=...
 WHATSAPP_APP_SECRET=...
-
-# xAI / Grok
-XAI_API_KEY=...

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

@@ -39,9 +39,7 @@ 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

@@ -39,10 +39,8 @@ 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=RimeHttpTTSService.Settings(
-                voice="rex",
-            ),
         )
 
         task = PipelineTask(

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

@@ -37,9 +37,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     task = PipelineTask(

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

@@ -29,9 +29,7 @@ async def main():
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     pipeline = Pipeline([tts, transport.output()])

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

@@ -37,9 +37,7 @@ async def main():
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     runner = PipelineRunner()

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

@@ -39,16 +39,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are an LLM in a WebRTC session, and this is a 'hello world' demo.",
     )
 
     task = PipelineTask(
@@ -60,7 +56,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         context = LLMContext()
-        context.add_message({"role": "developer", "content": "Say hello to the world."})
+        context.add_message({"role": "system", "content": "Say hello to the world."})
         await task.queue_frames([LLMContextFrame(context), EndFrame()])
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)

examples/foundational/03-still-frame.py

@@ -45,9 +45,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Create an HTTP session
     async with aiohttp.ClientSession() as session:
         imagegen = FalImageGenService(
-            settings=FalImageGenService.Settings(
-                image_size="square_hd",
-            ),
+            params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )

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

@@ -37,9 +37,7 @@ async def main():
         )
 
         imagegen = FalImageGenService(
-            settings=FalImageGenService.Settings(
-                image_size="square_hd",
-            ),
+            params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )

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

@@ -67,16 +67,12 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -109,9 +105,7 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
         # Kick off the conversation.
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
+        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/04a-transports-daily.py

@@ -50,16 +50,13 @@ async def main():
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaTTSService.Settings(
-                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-            ),
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
+            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()
@@ -92,7 +89,7 @@ async def main():
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

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

@@ -57,16 +57,12 @@ async def main():
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     context = LLMContext()

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

@@ -16,12 +16,11 @@ from pipecat.frames.frames import (
     Frame,
     LLMContextFrame,
     LLMFullResponseStartFrame,
-    OutputImageRawFrame,
     TextFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.sync_parallel_pipeline import FrameOrder, SyncParallelPipeline
+from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.sentence import SentenceAggregator
@@ -31,7 +30,6 @@ from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaHttpTTSService
 from pipecat.services.fal.image import FalImageGenService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.services.tts_service import TextAggregationMode
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 
@@ -46,18 +44,6 @@ class MonthFrame(DataFrame):
         return f"{self.name}(month: {self.month})"
 
 
-class MarkImageForPlaybackSync(FrameProcessor):
-    """Marks output image frames to be synchronized with audio playback."""
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, OutputImageRawFrame):
-            frame.sync_with_audio = True
-
-        await self.push_frame(frame, direction)
-
-
 class MonthPrepender(FrameProcessor):
     def __init__(self):
         super().__init__()
@@ -112,19 +98,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaHttpTTSService.Settings(
-                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-            ),
-            # No need to aggregate by sentences (the default), as we already know we're getting full sentences
-            # (Otherwise the service will unnecessarily wait for follow-up input to confirm the sentence is complete,
-            #  which, sadly, actually breaks the synchronization mechanism)
-            text_aggregation_mode=TextAggregationMode.TOKEN,
+            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
         )
 
         imagegen = FalImageGenService(
-            settings=FalImageGenService.Settings(
-                image_size="square_hd",
-            ),
+            params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )
@@ -137,26 +115,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # that, each pipeline runs concurrently and `SyncParallelPipeline` will
         # wait for the input frame to be processed.
         #
-        # We use `FrameOrder.PIPELINE` so that each synchronized batch of output
-        # frames is pushed in the order the pipelines are listed: image first,
-        # then audio. This ensures the transport receives the image before the
-        # audio frames it should accompany.
-        #
         # Note that `SyncParallelPipeline` requires the last processor in each
         # of the pipelines to be synchronous. In this case, we use
-        # `FalImageGenService` and `CartesiaHttpTTSService` which make HTTP
+        # `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
         # requests and wait for the response.
         pipeline = Pipeline(
             [
                 llm,  # LLM
                 sentence_aggregator,  # Aggregates LLM output into full sentences
                 SyncParallelPipeline(  # Run pipelines in parallel aggregating the result
-                    [
-                        imagegen,  # Generate image
-                        MarkImageForPlaybackSync(),  # Mark image as needing sync w/audio during playback
-                    ],
                     [month_prepender, tts],  # Create "Month: sentence" and output audio
-                    frame_order=FrameOrder.PIPELINE,
+                    [imagegen],  # Generate image
                 ),
                 transport.output(),  # Transport output
             ]
@@ -179,7 +148,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         ]:
             messages = [
                 {
-                    "role": "user",
+                    "role": "system",
                     "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
                 }
             ]

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

@@ -0,0 +1,198 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+import tkinter as tk
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    OutputAudioRawFrame,
+    TextFrame,
+    TTSAudioRawFrame,
+    URLImageRawFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
+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.fal.image import FalImageGenService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        tk_root = tk.Tk()
+        tk_root.title("Calendar")
+
+        runner = PipelineRunner()
+
+        async def get_month_data(month):
+            messages = [
+                {
+                    "role": "system",
+                    "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
+                }
+            ]
+
+            class ImageDescription(FrameProcessor):
+                def __init__(self):
+                    super().__init__()
+                    self.text = ""
+
+                async def process_frame(self, frame: Frame, direction: FrameDirection):
+                    await super().process_frame(frame, direction)
+
+                    if isinstance(frame, TextFrame):
+                        self.text = frame.text
+                    await self.push_frame(frame, direction)
+
+            class AudioGrabber(FrameProcessor):
+                def __init__(self):
+                    super().__init__()
+                    self.audio = bytearray()
+                    self.frame = None
+
+                async def process_frame(self, frame: Frame, direction: FrameDirection):
+                    await super().process_frame(frame, direction)
+
+                    if isinstance(frame, TTSAudioRawFrame):
+                        self.audio.extend(frame.audio)
+                        self.frame = OutputAudioRawFrame(
+                            bytes(self.audio), frame.sample_rate, frame.num_channels
+                        )
+                    await self.push_frame(frame, direction)
+
+            class ImageGrabber(FrameProcessor):
+                def __init__(self):
+                    super().__init__()
+                    self.frame = None
+
+                async def process_frame(self, frame: Frame, direction: FrameDirection):
+                    await super().process_frame(frame, direction)
+
+                    if isinstance(frame, URLImageRawFrame):
+                        self.frame = frame
+                    await self.push_frame(frame, direction)
+
+            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+            tts = CartesiaHttpTTSService(
+                api_key=os.getenv("CARTESIA_API_KEY"),
+                voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+            )
+
+            imagegen = FalImageGenService(
+                params=FalImageGenService.InputParams(image_size="square_hd"),
+                aiohttp_session=session,
+                key=os.getenv("FAL_KEY"),
+            )
+
+            sentence_aggregator = SentenceAggregator()
+
+            description = ImageDescription()
+
+            audio_grabber = AudioGrabber()
+
+            image_grabber = ImageGrabber()
+
+            # With `SyncParallelPipeline` we synchronize audio and images by
+            # pushing them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2
+            # I3 A3). To do that, each pipeline runs concurrently and
+            # `SyncParallelPipeline` will wait for the input frame to be
+            # processed.
+            #
+            # Note that `SyncParallelPipeline` requires the last processor in
+            # each of the pipelines to be synchronous. In this case, we use
+            # `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
+            # requests and wait for the response.
+            pipeline = Pipeline(
+                [
+                    llm,  # LLM
+                    sentence_aggregator,  # Aggregates LLM output into full sentences
+                    description,  # Store sentence
+                    SyncParallelPipeline(
+                        [tts, audio_grabber],  # Generate and store audio for the given sentence
+                        [imagegen, image_grabber],  # Generate and storeimage for the given sentence
+                    ),
+                ]
+            )
+
+            task = PipelineTask(pipeline)
+            await task.queue_frame(LLMContextFrame(LLMContext(messages)))
+            await task.stop_when_done()
+
+            await runner.run(task)
+
+            return {
+                "month": month,
+                "text": description.text,
+                "image": image_grabber.frame,
+                "audio": audio_grabber.frame,
+            }
+
+        transport = TkLocalTransport(
+            tk_root,
+            TkTransportParams(
+                audio_out_enabled=True,
+                video_out_enabled=True,
+                video_out_width=1024,
+                video_out_height=1024,
+            ),
+        )
+
+        pipeline = Pipeline([transport.output()])
+
+        task = PipelineTask(pipeline)
+
+        # We only specify a few months as we create tasks all at once and we
+        # might get rate limited otherwise.
+        months: list[str] = [
+            "January",
+            "February",
+        ]
+
+        # We create one task per month. This will be executed concurrently.
+        month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
+
+        # Now we wait for each month task in the order they're completed. The
+        # benefit is we'll have as little delay as possible before the first
+        # month, and likely no delay between months, but the months won't
+        # display in order.
+        async def show_images(month_tasks):
+            for month_data_task in asyncio.as_completed(month_tasks):
+                data = await month_data_task
+                await task.queue_frames([data["image"], data["audio"]])
+
+            await runner.stop_when_done()
+
+        async def run_tk():
+            while not task.has_finished():
+                tk_root.update()
+                tk_root.update_idletasks()
+                await asyncio.sleep(0.1)
+
+        await asyncio.gather(runner.run(task), show_images(month_tasks), run_tk())
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -83,16 +83,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -129,9 +125,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/06a-image-sync.py

@@ -100,16 +100,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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

@@ -6,7 +6,6 @@
 
 import os
 
-import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -53,68 +52,60 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    async with aiohttp.ClientSession() as session:
-        stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
+    stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
 
-        tts = CartesiaHttpTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            aiohttp_session=session,
-            settings=CartesiaHttpTTSService.Settings(
-                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-            ),
-        )
+    tts = CartesiaHttpTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
-        )
+    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.",
+    )
 
-        context = LLMContext()
-        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-            context,
-            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-        )
+    context = LLMContext()
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,
-                user_aggregator,  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                assistant_aggregator,  # Assistant spoken responses
-            ]
-        )
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            user_aggregator,  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            assistant_aggregator,  # Assistant spoken responses
+        ]
+    )
 
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
 
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
-            )
-            await task.queue_frames([LLMRunFrame()])
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMRunFrame()])
 
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        await runner.run(task)
+    await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):

examples/foundational/07-interruptible.py

@@ -24,6 +24,7 @@ from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.services.tts_service import TextAggregationMode
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -55,16 +56,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+        # Alternatively, you can use TextAggregationMode.TOKEN to stream tokens instead of
+        # sentencesfor faster response times.
+        # text_aggregation_mode=TextAggregationMode.TOKEN,
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -98,9 +98,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07a-interruptible-speechmatics-vad.py

@@ -21,6 +21,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
+from pipecat.services.openai.base_llm import BaseOpenAILLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
 from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
@@ -92,7 +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"),
-            settings=SpeechmaticsSTTService.Settings(
+            params=SpeechmaticsSTTService.InputParams(
                 language=Language.EN,
                 turn_detection_mode=SpeechmaticsSTTService.TurnDetectionMode.ADAPTIVE,
                 # focus_speakers=["S1"],
@@ -103,21 +104,32 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = SpeechmaticsTTSService(
             api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            settings=SpeechmaticsTTSService.Settings(
-                voice="sarah",
-            ),
+            voice_id="sarah",
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                temperature=0.75,
-                system_instruction="You are a helpful British assistant called Sarah in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. 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.",
-            ),
+            params=BaseOpenAILLMService.InputParams(temperature=0.75),
         )
 
-        context = LLMContext()
+        messages = [
+            {
+                "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()),
@@ -148,7 +160,7 @@ 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.
-            context.add_message({"role": "developer", "content": "Say a short hello to the user."})
+            messages.append({"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

@@ -22,6 +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.openai.base_llm import BaseOpenAILLMService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
 from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
@@ -75,7 +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"),
-            settings=SpeechmaticsSTTService.Settings(
+            params=SpeechmaticsSTTService.InputParams(
                 language=Language.EN,
                 speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
             ),
@@ -83,21 +84,31 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = SpeechmaticsTTSService(
             api_key=os.getenv("SPEECHMATICS_API_KEY"),
-            settings=SpeechmaticsTTSService.Settings(
-                voice="sarah",
-            ),
+            voice_id="sarah",
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                temperature=0.75,
-                system_instruction="You are a helpful British assistant called Sarah in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. 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.",
-            ),
+            params=BaseOpenAILLMService.InputParams(temperature=0.75),
         )
 
-        context = LLMContext()
+        messages = [
+            {
+                "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()),
@@ -128,7 +139,7 @@ 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.
-            context.add_message({"role": "developer", "content": "Say a short hello to the user."})
+            messages.append({"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

@@ -71,16 +71,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     prompt = ChatPromptTemplate.from_messages(
         [
             (
                 "system",
-                "You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
+                "Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
+                "Your response will be synthesized to voice and those characters will create unnatural sounds.",
             ),
             MessagesPlaceholder("chat_history"),
             ("human", "{input}"),

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

@@ -1,151 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.aws.llm import AWSBedrockLLMService, AWSBedrockLLMSettings
-from pipecat.services.deepgram.flux.sagemaker.stt import DeepgramFluxSageMakerSTTService
-from pipecat.services.deepgram.sagemaker.tts import DeepgramSageMakerTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
-
-load_dotenv(override=True)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    # Initialize Deepgram Flux SageMaker STT Service
-    # This requires:
-    # - AWS credentials configured (via environment variables or AWS CLI)
-    # - A deployed SageMaker endpoint with Deepgram Flux model
-    stt = DeepgramFluxSageMakerSTTService(
-        endpoint_name=os.getenv("SAGEMAKER_STT_ENDPOINT_NAME"),
-        region=os.getenv("AWS_REGION"),
-        settings=DeepgramFluxSageMakerSTTService.Settings(
-            min_confidence=0.3,
-        ),
-    )
-
-    # Initialize Deepgram SageMaker TTS Service
-    # This requires:
-    # - AWS credentials configured (via environment variables or AWS CLI)
-    # - A deployed SageMaker endpoint with Deepgram TTS model
-    tts = DeepgramSageMakerTTSService(
-        endpoint_name=os.getenv("SAGEMAKER_TTS_ENDPOINT_NAME"),
-        region=os.getenv("AWS_REGION"),
-        settings=DeepgramSageMakerTTSService.Settings(
-            voice="aura-2-andromeda-en",
-        ),
-    )
-
-    llm = AWSBedrockLLMService(
-        aws_region=os.getenv("AWS_REGION"),
-        settings=AWSBedrockLLMSettings(
-            model="us.amazon.nova-pro-v1:0",
-            temperature=0.8,
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
-    )
-
-    context = LLMContext()
-    # Use ExternalUserTurnStrategies since Flux handles turn detection natively
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(
-            user_turn_strategies=ExternalUserTurnStrategies(),
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    @stt.event_handler("on_update")
-    async def on_deepgram_flux_update(stt, transcript):
-        logger.debug(f"On deepgram flux update: {transcript}")
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -56,23 +56,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramFluxSTTService(
         api_key=os.getenv("DEEPGRAM_API_KEY"),
-        settings=DeepgramFluxSTTService.Settings(
-            min_confidence=0.3,
-        ),
+        params=DeepgramFluxSTTService.InputParams(min_confidence=0.3),
     )
 
-    tts = DeepgramTTSService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        settings=DeepgramTTSService.Settings(
-            voice="aura-2-andromeda-en",
-        ),
-    )
+    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -109,9 +100,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07c-interruptible-deepgram-http.py

@@ -59,20 +59,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = DeepgramHttpTTSService(
             api_key=os.getenv("DEEPGRAM_API_KEY"),
-            settings=DeepgramHttpTTSService.Settings(
-                voice="aura-2-andromeda-en",
-            ),
+            voice="aura-2-andromeda-en",
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
+            system_instruction="You are 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()
+        messages = []
+
+        context = LLMContext(messages)
         user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
             context,
             user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
@@ -104,7 +102,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

examples/foundational/07c-interruptible-deepgram-sagemaker.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.aws.llm import AWSBedrockLLMService, AWSBedrockLLMSettings
+from pipecat.services.aws.llm import AWSBedrockLLMService
 from pipecat.services.deepgram.sagemaker.stt import DeepgramSageMakerSTTService
 from pipecat.services.deepgram.sagemaker.tts import DeepgramSageMakerTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -69,18 +69,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     tts = DeepgramSageMakerTTSService(
         endpoint_name=os.getenv("SAGEMAKER_TTS_ENDPOINT_NAME"),
         region=os.getenv("AWS_REGION"),
-        settings=DeepgramSageMakerTTSService.Settings(
-            voice="aura-2-andromeda-en",
-        ),
+        voice="aura-2-andromeda-en",
     )
 
     llm = AWSBedrockLLMService(
         aws_region=os.getenv("AWS_REGION"),
-        settings=AWSBedrockLLMSettings(
-            model="us.amazon.nova-pro-v1:0",
-            temperature=0.8,
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        model="us.amazon.nova-pro-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.",
     )
 
     context = LLMContext()
@@ -114,9 +110,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07c-interruptible-deepgram-vad.py

@@ -7,6 +7,7 @@
 
 import os
 
+from deepgram import LiveOptions
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -55,24 +56,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(
         api_key=os.getenv("DEEPGRAM_API_KEY"),
-        settings=DeepgramSTTService.Settings(
-            vad_events=True,
-            utterance_end_ms="1000",
-        ),
+        live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
     )
 
-    tts = DeepgramTTSService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        settings=DeepgramTTSService.Settings(
-            voice="aura-2-andromeda-en",
-        ),
-    )
+    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -106,9 +97,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07c-interruptible-deepgram.py

@@ -55,18 +55,11 @@ 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"),
-        settings=DeepgramTTSService.Settings(
-            voice="aura-2-andromeda-en",
-        ),
-    )
+    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -100,9 +93,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07d-interruptible-elevenlabs-http.py

@@ -63,17 +63,13 @@ 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=ElevenLabsHttpTTSService.Settings(
-                voice=os.getenv("ELEVENLABS_VOICE_ID", ""),
-            ),
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
+            system_instruction="You are 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()
@@ -108,7 +104,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

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

@@ -57,16 +57,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = ElevenLabsTTSService(
         api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-        settings=ElevenLabsTTSService.Settings(
-            voice=os.getenv("ELEVENLABS_VOICE_ID", ""),
-        ),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -100,9 +96,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07e-interruptible-xai.py

@@ -1,128 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.xai.llm import GrokLLMService
-from pipecat.services.xai.tts import XAIHttpTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = XAIHttpTTSService(
-            api_key=os.getenv("XAI_API_KEY"),
-            aiohttp_session=session,
-            settings=XAIHttpTTSService.Settings(
-                voice="eve",
-            ),
-        )
-
-        llm = GrokLLMService(
-            api_key=os.getenv("XAI_API_KEY"),
-            settings=GrokLLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
-        )
-
-        context = LLMContext()
-        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-            context,
-            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-        )
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,
-                user_aggregator,  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                assistant_aggregator,  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
-
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
-            )
-            await task.queue_frames([LLMRunFrame()])
-
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
-
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-        await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -65,10 +65,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm = AzureLLMService(
         api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
         endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-        settings=AzureLLMService.Settings(
-            model=os.getenv("AZURE_CHATGPT_MODEL"),
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        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.",
     )
 
     context = LLMContext()
@@ -102,9 +100,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07f-interruptible-azure.py

@@ -65,10 +65,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm = AzureLLMService(
         api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
         endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-        settings=AzureLLMService.Settings(
-            model=os.getenv("AZURE_CHATGPT_MODEL"),
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        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.",
     )
 
     context = LLMContext()
@@ -102,9 +100,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07g-interruptible-openai-http.py

@@ -54,24 +54,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = OpenAISTTService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAISTTService.Settings(
-            model="gpt-4o-transcribe",
-            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"),
-        settings=OpenAITTSService.Settings(
-            voice="ballad",
-        ),
-    )
+    tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            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.",
-        ),
+        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()
@@ -106,9 +97,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07g-interruptible-openai.py

@@ -55,25 +55,20 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = OpenAIRealtimeSTTService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAIRealtimeSTTService.Settings(
-            model="gpt-4o-transcribe",
-            prompt="Expect words related to dogs, such as breed names.",
-            language=Language.EN,
-        ),
+        model="gpt-4o-transcribe",
+        prompt="Expect words related to dogs, such as breed names.",
+        language=Language.EN,
+        # Uses local VAD by default.
+        # 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"),
-        settings=OpenAITTSService.Settings(
-            voice="ballad",
-        ),
-    )
+    tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="ballad")
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            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.",
-        ),
+        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()
@@ -108,9 +103,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07h-interruptible-openpipe.py

@@ -57,9 +57,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     timestamp = int(time.time())
@@ -67,9 +65,7 @@ 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}"},
-        settings=OpenPipeLLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -103,9 +99,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07i-interruptible-xtts.py

@@ -59,17 +59,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = XTTSService(
             aiohttp_session=session,
-            settings=XTTSService.Settings(
-                voice="Claribel Dervla",
-            ),
+            voice_id="Claribel Dervla",
             base_url="http://localhost:8000",
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
+            system_instruction="You are 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()
@@ -104,7 +100,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

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

@@ -23,7 +23,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.gladia.config import LanguageConfig
+from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
 from pipecat.services.gladia.stt import GladiaSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transcriptions.language import Language
@@ -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"),
-        settings=GladiaSTTService.Settings(
+        params=GladiaInputParams(
             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", ""),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY", ""),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
-    )
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
 
-    context = LLMContext()
+    messages = [
+        {
+            "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(
@@ -114,9 +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.
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
+        messages.append({"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

@@ -23,7 +23,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.gladia.config import LanguageConfig
+from pipecat.services.gladia.config import GladiaInputParams, LanguageConfig
 from pipecat.services.gladia.stt import GladiaSTTService
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transcriptions.language import Language
@@ -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"),
-        settings=GladiaSTTService.Settings(
+        params=GladiaInputParams(
             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", ""),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY", ""),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
-    )
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""))
 
-    context = LLMContext()
+    messages = [
+        {
+            "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()),
@@ -109,9 +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.
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
+        messages.append({"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

@@ -54,18 +54,11 @@ 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"),
-        settings=LmntTTSService.Settings(
-            voice="morgan",
-        ),
-    )
+    tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -99,9 +92,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07l-interruptible-groq.py

@@ -56,10 +56,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = GroqLLMService(
         api_key=os.getenv("GROQ_API_KEY"),
-        settings=GroqLLMService.Settings(
-            model="llama-3.1-8b-instant",
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        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"))
@@ -95,9 +93,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07m-interruptible-aws-strands.py

@@ -95,16 +95,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = AWSPollyTTSService(
         region="us-west-2",  # only specific regions support generative TTS
-        settings=AWSPollyTTSService.Settings(
-            voice="Joanna",
-            engine="generative",
-            rate="1.1",
-        ),
+        voice_id="Joanna",
+        params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
     )
 
     # Create Strands agent processor
     try:
-        agent = build_agent(model_id="us.anthropic.claude-sonnet-4-6", max_tokens=8000)
+        agent = build_agent(model_id="us.anthropic.claude-3-5-haiku-20241022-v1:0", max_tokens=8000)
         llm = StrandsAgentsProcessor(agent=agent)
         logger.info("Successfully created Strands agent for NAB customer service coaching")
     except Exception as e:
@@ -151,8 +148,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                 LLMMessagesAppendFrame(
                     messages=[
                         {
-                            "role": "developer",
-                            "content": f"Greet the user and introduce yourself. Don't use emojis.",
+                            "role": "user",
+                            "content": f"Greet the user and introduce yourself.",
                         }
                     ],
                     run_llm=True,

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

@@ -54,20 +54,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = AWSPollyTTSService(
         region="us-west-2",  # only specific regions support generative TTS
-        settings=AWSPollyTTSService.Settings(
-            voice="Joanna",
-            engine="generative",
-            rate="1.1",
-        ),
+        voice_id="Joanna",
+        params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
     )
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        settings=AWSBedrockLLMService.Settings(
-            model="us.anthropic.claude-sonnet-4-6",
-            temperature=0.8,
-            # system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        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.",
     )
 
     context = LLMContext()
@@ -101,9 +96,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
+        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

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

@@ -70,27 +70,21 @@ 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=GoogleSTTService.Settings(
-            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=GoogleTTSService.Settings(
-            voice="en-US-Chirp3-HD-Charon",
-            language=Language.EN_US,
-        ),
     )
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GoogleLLMService.Settings(
-            model="gemini-2.5-flash-image",
-            # model="gemini-3-pro-image-preview", # A more powerful model, but slower,
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        model="gemini-2.5-flash-image",
+        # 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()
@@ -124,9 +118,7 @@ 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 with a styled introduction
-        context.add_message(
-            {"role": "developer", "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/07n-interruptible-gemini.py

@@ -54,17 +54,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot with Gemini TTS")
 
     stt = GoogleSTTService(
-        settings=GoogleSTTService.Settings(
-            languages=[Language.EN_US],
-        ),
+        params=GoogleSTTService.InputParams(languages=Language.EN_US),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
     )
 
     tts = GeminiTTSService(
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
-        settings=GeminiTTSService.Settings(
-            model="gemini-2.5-flash-tts",
-            voice="Charon",
+        model="gemini-2.5-flash-tts",
+        voice_id="Charon",
+        params=GeminiTTSService.InputParams(
             language=Language.EN_US,
             prompt="You are a helpful AI assistant. Speak in a natural, conversational tone.",
         ),
@@ -73,8 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
         model="gemini-2.5-flash",
-        settings=GoogleLLMService.Settings(
-            system_instruction="""You are a helpful assistant in a voice conversation.
+        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
@@ -91,8 +88,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             - "[whispering] Let me tell you a secret."
             - "The answer is... [long pause] ...42!"
 
-            Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Keep responses concise. Respond to what the user said in a creative and helpful 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()
@@ -128,7 +124,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # Kick off the conversation
         context.add_message(
             {
-                "role": "developer",
+                "role": "system",
                 "content": "You are an AI assistant. You can help with a variety of tasks. Introduce yourself and ask the user what they would like to know.",
             }
         )

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

@@ -54,31 +54,25 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
     stt = GoogleSTTService(
-        settings=GoogleSTTService.Settings(
-            languages=[Language.EN_US],
-            # Add model to use a specific model
-            # model="chirp_3",
-        ),
+        params=GoogleSTTService.InputParams(languages=Language.EN_US, model="chirp_3"),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
         location="us",
     )
 
     tts = GoogleHttpTTSService(
-        settings=GoogleHttpTTSService.Settings(
-            voice="en-US-Chirp3-HD-Charon",
-            language=Language.EN_US,
-        ),
+        voice_id="en-US-Chirp3-HD-Charon",
+        params=GoogleHttpTTSService.InputParams(language=Language.EN_US),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
     )
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GoogleLLMService.Settings(
-            model="gemini-2.5-flash",
-            # force a certain amount of thinking if you want it
-            # thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        model="gemini-2.5-flash",
+        # force a certain amount of thinking if you want it
+        # params=GoogleLLMService.InputParams(
+        #     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.",
     )
 
     context = LLMContext()
@@ -112,9 +106,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07n-interruptible-google.py

@@ -54,31 +54,25 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
     stt = GoogleSTTService(
-        settings=GoogleSTTService.Settings(
-            languages=[Language.EN_US],
-            # Add model to use a specific model
-            # model="chirp_3",
-        ),
+        params=GoogleSTTService.InputParams(languages=Language.EN_US, model="chirp_3"),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
         location="us",
     )
 
     tts = GoogleTTSService(
-        settings=GoogleTTSService.Settings(
-            voice="en-US-Chirp3-HD-Charon",
-            language=Language.EN_US,
-        ),
+        voice_id="en-US-Chirp3-HD-Charon",
+        params=GoogleTTSService.InputParams(language=Language.EN_US),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
     )
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GoogleLLMService.Settings(
-            model="gemini-2.5-flash",
-            # force a certain amount of thinking if you want it
-            # thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096),
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        model="gemini-2.5-flash",
+        # force a certain amount of thinking if you want it
+        # params=GoogleLLMService.InputParams(
+        #     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.",
     )
 
     context = LLMContext()
@@ -112,9 +106,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07o-interruptible-assemblyai-turn-detection.py

@@ -22,6 +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.assemblyai.models import AssemblyAIConnectionParams
 from pipecat.services.assemblyai.stt import AssemblyAISTTService
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
@@ -93,13 +94,13 @@ 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
-        settings=AssemblyAISTTService.Settings(
-            model="u3-rt-pro",
+        connection_params=AssemblyAIConnectionParams(
+            speech_model="u3-rt-pro",
             # Optional: Tune turn detection timing (defaults shown below)
             # min_turn_silence=100,  # Default
             # max_turn_silence=1000,  # Default
             # Optional: Boost accuracy for specific names/terms
-            # keyterms_prompt=["Xiomara", "Saoirse", "Krzystof", "API", "OAuth"],
+            # prompt="Names: Xiomara, Saoirse, Krzystof. Technical terms: API, OAuth.",
             # Optional: Enable speaker diarization
             # speaker_labels=True,
         ),
@@ -107,16 +108,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -153,9 +150,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07o-interruptible-assemblyai.py

@@ -59,16 +59,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -102,9 +98,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07p-interruptible-krisp-viva.py

@@ -84,17 +84,12 @@ 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"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -104,7 +99,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             user_turn_strategies=UserTurnStrategies(
                 stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=KrispVivaTurn())]
             ),
-            vad_analyzer=SileroVADAnalyzer(),  # or KrispVivaVadAnalyzer
+            vad_analyzer=SileroVADAnalyzer(),
         ),
     )
 
@@ -134,9 +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.
-        context.add_message(
-            {"role": "developer", "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/07p-interruptible-krisp.py

@@ -58,18 +58,11 @@ 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"),
-        settings=DeepgramTTSService.Settings(
-            voice="aura-helios-en",
-        ),
-    )
+    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -103,9 +96,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07q-interruptible-rime-http.py

@@ -60,19 +60,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = RimeHttpTTSService(
             api_key=os.getenv("RIME_API_KEY", ""),
-            settings=RimeHttpTTSService.Settings(
-                voice="luna",
-                model="arcana",
-            ),
+            voice_id="luna",
             model="arcana",
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
+            system_instruction="You are 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()
@@ -107,7 +102,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

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

@@ -56,16 +56,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = RimeTTSService(
         api_key=os.getenv("RIME_API_KEY", ""),
-        settings=RimeTTSService.Settings(
-            voice="luna",
-        ),
+        voice_id="luna",
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -99,9 +95,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07r-interruptible-nvidia.py

@@ -56,10 +56,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = NvidiaLLMService(
         api_key=os.getenv("NVIDIA_API_KEY"),
-        settings=NvidiaLLMService.Settings(
-            model="meta/llama-3.3-70b-instruct",
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        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"))
@@ -95,9 +93,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07s-interruptible-google-audio-in.py

@@ -48,7 +48,7 @@ load_dotenv(override=True)
 
 marker = "|----|"
 system_message = f"""
-You are a helpful LLM in a voice call. Your goals are to be helpful and brief in your responses.
+You are a helpful LLM in a WebRTC call. Your goals are to be helpful and brief in your responses.
 
 You are expert at transcribing audio to text. You will receive a mixture of audio and text input. When
 asked to transcribe what the user said, output an exact, word-for-word transcription.
@@ -216,24 +216,31 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GoogleLLMService.Settings(
-            model="gemini-2.5-flash",
-            system_instruction=system_message,
-            # force a certain amount of thinking if you want it
-            # thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
-        ),
+        model="gemini-2.5-flash",
+        # force a certain amount of thinking if you want it
+        # params=GoogleLLMService.InputParams(
+        #     thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
+        # ),
     )
 
     tts = GoogleTTSService(
-        settings=GoogleTTSService.Settings(
-            voice="en-US-Chirp3-HD-Charon",
-            language=Language.EN_US,
-        ),
+        voice_id="en-US-Chirp3-HD-Charon",
         params=GoogleTTSService.InputParams(language=Language.EN_US),
         credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
     )
 
-    context = LLMContext()
+    messages = [
+        {
+            "role": "system",
+            "content": system_message,
+        },
+        {
+            "role": "user",
+            "content": "Start by saying hello.",
+        },
+    ]
+
+    context = LLMContext(messages)
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
@@ -269,9 +276,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")

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

@@ -57,16 +57,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = FishAudioTTSService(
         api_key=os.getenv("FISH_API_KEY"),
-        settings=FishAudioTTSService.Settings(
-            voice="4ce7e917cedd4bc2bb2e6ff3a46acaa1",  # Barack Obama
-        ),
+        model="4ce7e917cedd4bc2bb2e6ff3a46acaa1",  # Barack Obama
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -100,9 +96,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07v-interruptible-neuphonic-http.py

@@ -60,17 +60,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = NeuphonicHttpTTSService(
             api_key=os.getenv("NEUPHONIC_API_KEY"),
-            settings=NeuphonicHttpTTSService.Settings(
-                voice="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
-            ),
+            voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
+            system_instruction="You are 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()
@@ -105,7 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

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

@@ -56,16 +56,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = NeuphonicTTSService(
         api_key=os.getenv("NEUPHONIC_API_KEY"),
-        settings=NeuphonicTTSService.Settings(
-            voice="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
-        ),
+        voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb",  # Emily
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -99,9 +95,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07w-interruptible-fal.py

@@ -7,7 +7,6 @@
 
 import os
 
-import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -54,70 +53,62 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    async with aiohttp.ClientSession() as session:
-        stt = FalSTTService(
-            api_key=os.getenv("FAL_KEY"),
-            aiohttp_session=session,
-        )
+    stt = FalSTTService(
+        api_key=os.getenv("FAL_KEY"),
+    )
 
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            settings=CartesiaTTSService.Settings(
-                voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-            ),
-        )
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
-        )
+    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.",
+    )
 
-        context = LLMContext()
-        user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-            context,
-            user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-        )
+    context = LLMContext()
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context,
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
+    )
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                user_aggregator,  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                assistant_aggregator,  # Assistant spoken responses
-            ]
-        )
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # STT
+            user_aggregator,  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            assistant_aggregator,  # Assistant spoken responses
+        ]
+    )
 
-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-        )
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
 
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
-            )
-            await task.queue_frames([LLMRunFrame()])
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMRunFrame()])
 
-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
-            await task.cancel()
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
 
-        runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-        await runner.run(task)
+    await runner.run(task)
 
 
 async def bot(runner_args: RunnerArguments):

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

@@ -44,16 +44,12 @@ async def main():
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -82,7 +78,7 @@ async def main():
         ),
     )
 
-    context.add_message({"role": "developer", "content": "Please introduce yourself to the user."})
+    context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
     await task.queue_frames([LLMRunFrame()])
 
     runner = PipelineRunner()

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

@@ -63,16 +63,12 @@ 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,
-            settings=MiniMaxHttpTTSService.Settings(
-                language=Language.EN,
-            ),
+            params=MiniMaxHttpTTSService.InputParams(language=Language.EN),
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
+            system_instruction="You are 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()
@@ -107,7 +103,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

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

@@ -23,7 +23,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.sarvam.llm import SarvamLLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.sarvam.stt import SarvamSTTService
 from pipecat.services.sarvam.tts import SarvamHttpTTSService
 from pipecat.transcriptions.language import Language
@@ -59,24 +59,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async with aiohttp.ClientSession() as session:
         stt = SarvamSTTService(
             api_key=os.getenv("SARVAM_API_KEY"),
-            settings=SarvamSTTService.Settings(
-                model="saarika:v2.5",
-            ),
+            model="saarika:v2.5",
         )
 
         tts = SarvamHttpTTSService(
             api_key=os.getenv("SARVAM_API_KEY"),
             aiohttp_session=session,
-            settings=SarvamHttpTTSService.Settings(
-                language=Language.EN_IN,
-            ),
+            params=SarvamHttpTTSService.InputParams(language=Language.EN),
         )
 
-        llm = SarvamLLMService(
-            api_key=os.getenv("SARVAM_API_KEY"),
-            settings=SarvamLLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
+        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.",
         )
 
         context = LLMContext()
@@ -111,7 +105,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             context.add_message(
-                {"role": "user", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

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

@@ -21,7 +21,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.sarvam.llm import SarvamLLMService
+from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.sarvam.stt import SarvamSTTService
 from pipecat.services.sarvam.tts import SarvamTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -54,23 +54,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = SarvamSTTService(
         api_key=os.getenv("SARVAM_API_KEY"),
-        settings=SarvamSTTService.Settings(
-            model="saaras:v3",
-        ),
+        model="saarika:v2.5",
     )
 
     tts = SarvamTTSService(
         api_key=os.getenv("SARVAM_API_KEY"),
-        settings=SarvamTTSService.Settings(
-            model="bulbul:v3",
-            voice="shubh",
-        ),
+        model="bulbul:v2",
+        voice_id="manisha",
     )
-    llm = SarvamLLMService(
-        api_key=os.getenv("SARVAM_API_KEY"),
-        settings=SarvamLLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+    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.",
     )
 
     context = LLMContext()
@@ -96,7 +90,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         params=PipelineParams(
             enable_metrics=True,
             enable_usage_metrics=True,
-            allow_interruptions=True,
         ),
     )
 
@@ -104,7 +97,7 @@ 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.
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
+        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
         # Optionally, you can wait for 30 seconds and then change the voice.

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

@@ -24,7 +24,7 @@ from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.services.soniox.stt import SonioxSTTService
+from pipecat.services.soniox.stt import SonioxInputParams, SonioxSTTService
 from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
@@ -53,9 +53,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = SonioxSTTService(
         api_key=os.getenv("SONIOX_API_KEY"),
-        settings=SonioxSTTService.Settings(
-            # Add language hints to use a specific language
-            # Add strict mode to enforce the language hints
+        params=SonioxInputParams(
             language_hints=[Language.EN],
             language_hints_strict=True,
         ),
@@ -63,16 +61,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -105,9 +99,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07zb-interruptible-inworld-http.py

@@ -58,18 +58,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         tts = InworldHttpTTSService(
             api_key=os.getenv("INWORLD_API_KEY", ""),
             aiohttp_session=session,
-            streaming=True,
-            settings=InworldHttpTTSService.Settings(
-                voice="Ashley",
-            ),
+            voice_id="Ashley",
+            model="inworld-tts-1",
             # Set to False for non-streaming mode or True for streaming mode.
+            streaming=True,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                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.",
-            ),
+            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()
@@ -111,7 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info("Client connected")
             # Kick off the conversation.
             context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

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

@@ -10,7 +10,8 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
+from pipecat.frames.frames import LLMRunFrame, TTSTextFrame
+from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -24,6 +25,7 @@ from pipecat.runner.utils import create_transport
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.inworld.tts import InworldTTSService
 from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_output import BaseOutputTransport
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -54,17 +56,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = InworldTTSService(
         api_key=os.getenv("INWORLD_API_KEY", ""),
-        settings=InworldTTSService.Settings(
-            voice="Ashley",
-            temperature=1.1,
-        ),
+        voice_id="Ashley",
+        model="inworld-tts-1",
+        temperature=1.1,
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            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.",
-        ),
+        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()
@@ -91,6 +90,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             enable_metrics=True,
             enable_usage_metrics=True,
         ),
+        observers=[
+            DebugLogObserver(
+                frame_types={
+                    TTSTextFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
+                }
+            ),
+        ],
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
 
@@ -98,9 +104,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info("Client connected")
         # Kick off the conversation.
-        context.add_message(
-            {"role": "developer", "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/07zc-interruptible-asyncai-http.py

@@ -60,17 +60,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
         tts = AsyncAIHttpTTSService(
             api_key=os.getenv("ASYNCAI_API_KEY", ""),
-            settings=AsyncAIHttpTTSService.Settings(
-                voice="e0f39dc4-f691-4e78-bba5-5c636692cc04",
-            ),
+            voice_id=os.getenv("ASYNCAI_VOICE_ID", "e0f39dc4-f691-4e78-bba5-5c636692cc04"),
             aiohttp_session=session,
         )
 
         llm = OpenAILLMService(
             api_key=os.getenv("OPENAI_API_KEY"),
-            settings=OpenAILLMService.Settings(
-                system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-            ),
+            system_instruction="You are 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()
@@ -105,7 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             logger.info(f"Client connected")
             # Kick off the conversation.
             context.add_message(
-                {"role": "developer", "content": "Please introduce yourself to the user."}
+                {"role": "system", "content": "Please introduce yourself to the user."}
             )
             await task.queue_frames([LLMRunFrame()])
 

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

@@ -57,16 +57,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = AsyncAITTSService(
         api_key=os.getenv("ASYNCAI_API_KEY", ""),
-        settings=AsyncAITTSService.Settings(
-            voice="e0f39dc4-f691-4e78-bba5-5c636692cc04",
-        ),
+        voice_id=os.getenv("ASYNCAI_VOICE_ID", "e0f39dc4-f691-4e78-bba5-5c636692cc04"),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -100,9 +96,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07zd-interruptible-aicoustics.py

@@ -77,16 +77,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -128,9 +124,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         await audiobuffer.start_recording()
         # Kick off the conversation.
-        context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
-        )
+        context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
     @audiobuffer.event_handler("on_audio_data")

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

@@ -59,16 +59,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     tts = HumeTTSService(
         api_key=os.getenv("HUME_API_KEY"),
         # Replace with your Hume voice ID
-        settings=HumeTTSService.Settings(
-            voice="f898a92e-685f-43fa-985b-a46920f0650b",
-        ),
+        voice_id="f898a92e-685f-43fa-985b-a46920f0650b",
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -113,9 +109,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             "💡 Word timestamps are enabled! Watch the console for TTSTextFrame logs showing each word with its PTS."
         )
         # Kick off the conversation.
-        context.add_message(
-            {"role": "developer", "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/07zf-interruptible-gradium.py

@@ -55,24 +55,20 @@ 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",
-        settings=GradiumSTTService.Settings(
+        params=GradiumSTTService.InputParams(
             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=GradiumTTSService.Settings(
-            voice="YTpq7expH9539ERJ",
-        ),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -106,9 +102,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07zg-interruptible-camb.py

@@ -56,16 +56,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CambTTSService(
         api_key=os.getenv("CAMB_API_KEY"),
-        settings=CambTTSService.Settings(
-            model="mars-flash",
-        ),
+        model="mars-flash",
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful voice assistant powered by Camb AI text-to-speech. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Keep responses concise.",
-        ),
+        system_instruction="You are a helpful voice assistant powered by Camb AI text-to-speech. ",
     )
 
     context = LLMContext()
@@ -99,9 +95,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info("Client connected")
-        context.add_message(
-            {"role": "developer", "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/07zh-interruptible-hathora.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2024-2026, Daily
+# Copyright (c) 2024–2026, Daily
 #
 # SPDX-License-Identifier: BSD 2-Clause License
 #
@@ -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.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.responses.llm import OpenAIResponsesLLMService
+from pipecat.services.hathora.stt import HathoraSTTService
+from pipecat.services.hathora.tts import HathoraTTSService
+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
@@ -51,24 +51,24 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+    stt = HathoraSTTService(
+        model="nvidia-parakeet-tdt-0.6b-v3",
     )
 
-    llm = OpenAIResponsesLLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAIResponsesLLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+    tts = HathoraTTSService(
+        model="hexgrad-kokoro-82m",
+    )
+
+    # See https://models.hathora.dev/model/qwen3-30b-a3b
+    llm = OpenAILLMService(
+        base_url="https://app-362f7ca1-6975-4e18-a605-ab202bf2c315.app.hathora.dev/v1",
+        api_key=os.getenv("HATHORA_API_KEY"),
+        model=None,
+        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()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+    context_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
     )
@@ -77,11 +77,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         [
             transport.input(),  # Transport user input
             stt,
-            user_aggregator,  # User responses
+            context_aggregator.user(),  # User responses
             llm,  # LLM
             tts,  # TTS
             transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
+            context_aggregator.assistant(),  # Assistant spoken responses
         ]
     )
 
@@ -98,9 +98,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07zi-interruptible-piper.py

@@ -54,17 +54,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = PiperTTSService(
-        settings=PiperTTSService.Settings(
-            voice="en_US-ryan-high",
-        ),
-    )
+    tts = PiperTTSService(voice_id="en_US-ryan-high")
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -98,9 +92,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07zj-interruptible-kokoro.py

@@ -54,17 +54,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-    tts = KokoroTTSService(
-        settings=KokoroTTSService.Settings(
-            voice="af_heart",
-        ),
-    )
+    tts = KokoroTTSService(voice_id="af_heart")
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -98,9 +92,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07zk-interruptible-resemble.py

@@ -30,20 +30,24 @@ from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 
 load_dotenv(override=True)
 
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
 transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
@@ -55,16 +59,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = ResembleAITTSService(
         api_key=os.getenv("RESEMBLE_API_KEY"),
-        settings=ResembleAITTSService.Settings(
-            voice=os.getenv("RESEMBLE_VOICE_UUID"),
-        ),
+        voice_id=os.getenv("RESEMBLE_VOICE_UUID"),
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -98,9 +98,7 @@ 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.
-        context.add_message(
-            {"role": "developer", "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/07zl-interruptible-smallest.py

@@ -1,122 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-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.smallest.tts import SmallestTTSService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-    )
-
-    tts = SmallestTTSService(
-        api_key=os.getenv("SMALLEST_API_KEY"),
-        settings=SmallestTTSService.Settings(
-            voice="sophia",
-        ),
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            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()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            user_aggregator,
-            llm,
-            tts,
-            transport.output(),
-            assistant_aggregator,
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -95,16 +95,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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()
@@ -141,9 +137,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     async def on_client_connected(transport, client):
         logger.info(f"Client connected: {client}")
         # Kick off the conversation.
-        context.add_message(
-            {"role": "developer", "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/10-wake-phrase.py

@@ -10,7 +10,7 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
+from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -19,6 +19,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
     LLMUserAggregatorParams,
 )
+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
@@ -27,11 +28,6 @@ from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.turns.user_start import WakePhraseUserTurnStartStrategy
-from pipecat.turns.user_turn_strategies import (
-    UserTurnStrategies,
-    default_user_turn_start_strategies,
-)
 
 load_dotenv(override=True)
 
@@ -56,49 +52,31 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = DeepgramSTTService(
-        api_key=os.getenv("DEEPGRAM_API_KEY"),
-        settings=DeepgramSTTService.Settings(
-            keyterm=["pipecat"],
-        ),
-    )
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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"])
+
     context = LLMContext()
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
-        user_params=LLMUserAggregatorParams(
-            user_turn_strategies=UserTurnStrategies(
-                start=[
-                    WakePhraseUserTurnStartStrategy(
-                        phrases=["pipecat"],
-                        # Timeout before wake phrase must be spoken again
-                        timeout=5.0,
-                    ),
-                    *default_user_turn_start_strategies(),
-                ]
-            ),
-            vad_analyzer=SileroVADAnalyzer(),
-        ),
+        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
     )
 
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
-            stt,
+            stt,  # STT
+            hey_robot_filter,  # Filter out speech not directed at the robot
             user_aggregator,  # User responses
             llm,  # LLM
             tts,  # TTS
@@ -121,7 +99,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         logger.info(f"Client connected")
         # Kick off the conversation.
         context.add_message(
-            {"role": "developer", "content": "Please introduce yourself to the user."}
+            {
+                "role": "system",
+                "content": "Please introduce yourself. Tell the user they should say 'Hey Robot' before talking to you.",
+            }
         )
         await task.queue_frames([LLMRunFrame()])
 

examples/foundational/11-sound-effects.py

@@ -106,16 +106,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
-        ),
+        system_instruction="You are 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"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     context = LLMContext()

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

@@ -1,139 +0,0 @@
-#
-# Copyright (c) 2024-2026, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-from PIL import Image
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import (
-    LLMContextAggregatorPair,
-    LLMUserAggregatorParams,
-)
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.responses.llm import OpenAIResponsesLLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-# We use lambdas to defer transport parameter creation until the transport
-# type is selected at runtime.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
-    )
-
-    llm = OpenAIResponsesLLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAIResponsesLLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way. You are also able to describe images.",
-        ),
-    )
-
-    context = LLMContext()
-    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
-    )
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            user_aggregator,  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            assistant_aggregator,  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-
-        if not runner_args.body:
-            script_dir = os.path.dirname(__file__)
-            runner_args.body = {
-                "image_path": os.path.join(script_dir, "assets", "cat.jpg"),
-                "question": "Describe this image",
-            }
-
-        image_path = runner_args.body["image_path"]
-        question = runner_args.body["question"]
-
-        # Kick off the conversation.
-        image = Image.open(image_path)
-        message = await LLMContext.create_image_message(
-            image=image.tobytes(),
-            format="RGB",
-            size=image.size,
-            text=question,
-        )
-        context.add_message(message)
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

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

@@ -53,16 +53,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = OpenAILLMService(
         api_key=os.getenv("OPENAI_API_KEY"),
-        settings=OpenAILLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way. You are also able to describe images.",
-        ),
+        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

@@ -53,16 +53,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = AnthropicLLMService(
         api_key=os.getenv("ANTHROPIC_API_KEY"),
-        settings=AnthropicLLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way. You are also able to describe images.",
-        ),
+        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

@@ -53,18 +53,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        settings=AWSBedrockLLMService.Settings(
-            model="us.anthropic.claude-sonnet-4-6",
-            temperature=0.8,
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way. You are also able to describe images.",
-        ),
+        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        # Note: usually, prefer providing latency="optimized" param.
+        # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
+        # which we need for image input.
+        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        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

@@ -53,16 +53,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     llm = GoogleLLMService(
         api_key=os.getenv("GOOGLE_API_KEY"),
-        settings=GoogleLLMService.Settings(
-            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way. You are also able to describe images.",
-        ),
+        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

@@ -42,9 +42,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     tts = CartesiaTTSService(
         api_key=os.getenv("CARTESIA_API_KEY"),
-        settings=CartesiaTTSService.Settings(
-            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-        ),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
     )
 
     vision = MoondreamService()

examples/foundational/13-whisper-transcription.py

@@ -13,7 +13,6 @@ from pipecat.frames.frames import Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.audio.vad_processor import VADProcessor
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -41,12 +40,15 @@ class TranscriptionLogger(FrameProcessor):
 transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "twilio": lambda: FastAPIWebsocketParams(
         audio_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
     ),
 }
 
@@ -57,9 +59,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     stt = WhisperSTTService()
 
     tl = TranscriptionLogger()
-    vad_processor = VADProcessor(vad_analyzer=SileroVADAnalyzer())
 
-    pipeline = Pipeline([transport.input(), vad_processor, stt, tl])
+    pipeline = Pipeline([transport.input(), stt, tl])
 
     task = PipelineTask(
         pipeline,

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

@@ -15,7 +15,6 @@ from pipecat.frames.frames import Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.audio.vad_processor import VADProcessor
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.whisper.stt import WhisperSTTService
 from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
@@ -41,15 +40,15 @@ async def main():
     transport = LocalAudioTransport(
         LocalAudioTransportParams(
             audio_in_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
         )
     )
 
     stt = WhisperSTTService()
 
     tl = TranscriptionLogger()
-    vad_processor = VADProcessor(vad_analyzer=SileroVADAnalyzer())
 
-    pipeline = Pipeline([transport.input(), vad_processor, stt, tl])
+    pipeline = Pipeline([transport.input(), stt, tl])
 
     task = PipelineTask(pipeline)