Fetch full git history for setuptools_scm version detection

.github/workflows/build.yaml

@@ -21,20 +21,21 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      
+        with:
+          fetch-depth: 0 # Fetch all history for setuptools_scm
       - name: Install uv
         uses: astral-sh/setup-uv@v3
         with:
           version: "latest"
-      
+
       - name: Set up Python
         run: uv python install 3.10
-      
+
       - name: Install development dependencies
         run: uv sync --group dev
-      
+
       - name: Build project
         run: uv build
-      
+
       - name: Install project in editable mode
-        run: uv pip install --editable .
+        run: uv pip install --editable .

CHANGELOG.md

@@ -5,16 +5,17 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.0.91] - 2025-10-21
+## [Unreleased]
 
 ### Added
 
-- It is now possible to start a bot from the `/start` endpoint when using the
-  runner Daily's transport. This follows the Pipecat Cloud format with
-  `createDailyRoom` and `body` fields in the POST request body.
+- Added the [Pipecat CLI](https://github.com/pipecat-ai/pipecat-cli) to the
+  required dependencies, enabling you to scaffold a new project directly from
+  `pipecat-ai`. Get started with:
 
-- Added an ellipsis character (`…`) to the end of sentence detection in the
-  string utils.
+  ```bash
+  uv run pipecat init
+  ```
 
 - Expanded support for universal `LLMContext` to `AWSNovaSonicLLMService`.
   As a reminder, the context-setup pattern when using `LLMContext` is:
@@ -45,6 +46,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   # or
   context: OpenAILLMContext
 
+  # Reading messages from context
+  messages = context.messages
+
   ## AFTER:
 
   # Context aggregator type
@@ -55,6 +59,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
   # Context type
   context: LLMContext
+
+  # Reading messages from context
+  messages = context.get_messages()
   ```
 
 - Added support for `bulbul:v3` model in `SarvamTTSService` and
@@ -86,9 +93,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
-- `RunnerArguments` now include the `body` field, so there's no need to add it
-  to subclasses. Also, all `RunnerArguments` fields are now keyword-only.
-
 - `CartesiaSTTService` now inherits from `WebsocketSTTService`.
 
 - Package upgrades:
@@ -110,17 +114,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
-- Fixed an issue where the `RTVIProcessor` was sending duplicate
-  `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` messages.
-
-- Fixed an issue in `AWSBedrockLLMService` where both `temperature` and `top_p`
-  were always sent together, causing conflicts with models like Claude Sonnet 4.5
-  that don't allow both parameters simultaneously. The service now only includes
-  inference parameters that are explicitly set, and `InputParams` defaults have
-  been changed to `None` to rely on AWS Bedrock's built-in model defaults.
-
 - Fixed an issue in `RivaSegmentedSTTService` where a runtime error occurred due
-  to a mismatch in the `_handle_transcription` method's signature.
+  to a mismatch in the \_handle_transcription method's signature.
 
 - Fixed multiple pipeline task cancellation issues. `asyncio.CancelledError` is
   now handled properly in `PipelineTask` making it possible to cancel an asyncio

README.md

@@ -44,7 +44,7 @@ Looking to build structured conversations? Check out [Pipecat Flows](https://git
 
 Want to build beautiful and engaging experiences? Checkout the [Voice UI Kit](https://github.com/pipecat-ai/voice-ui-kit), a collection of components, hooks and templates for building voice AI applications quickly.
 
-### 🛠️ Create and deploy projects
+### 🛠️ CLI
 
 Create a new project in under a minute with the [Pipecat CLI](https://github.com/pipecat-ai/pipecat-cli). Then use the CLI to monitor and deploy your agent to production.
 

env.example

@@ -4,9 +4,6 @@ AICOUSTICS_LICENSE_KEY=...
 # Anthropic
 ANTHROPIC_API_KEY=...
 
-# Assembly AI
-ASSEMBLYAI_API_KEY=...
-
 # Async
 ASYNCAI_API_KEY=...
 ASYNCAI_VOICE_ID=...
@@ -24,19 +21,12 @@ AZURE_CHATGPT_API_KEY=...
 AZURE_CHATGPT_ENDPOINT=https://...
 AZURE_CHATGPT_MODEL=...
 
-AZURE_REALTIME_API_KEY=...
-AZURE_REALTIME_BASE_URL=...
-
 AZURE_DALLE_API_KEY=...
 AZURE_DALLE_ENDPOINT=https://...
 AZURE_DALLE_MODEL=...
 
 # Cartesia
 CARTESIA_API_KEY=...
-CARTESIA_VOICE_ID=...
-
-# Cerebras
-CEREBRAS_API_KEY=...
 
 # Daily
 DAILY_API_KEY=...
@@ -45,75 +35,42 @@ DAILY_SAMPLE_ROOM_URL=https://...
 # Deepgram
 DEEPGRAM_API_KEY=...
 
-# DeepSeek
-DEEPSEEK_API_KEY=...
-
 # ElevenLabs
 ELEVENLABS_API_KEY=...
 ELEVENLABS_VOICE_ID=...
 
+# Neuphonic
+NEUPHONIC_API_KEY=...
+
 # Fal
 FAL_KEY=...
 
 # Fireworks
 FIREWORKS_API_KEY=...
 
-# Fish Audio
-FISH_API_KEY=...
-
 # Gladia
 GLADIA_API_KEY=...
 GLADIA_REGION=...
 
 # Google
 GOOGLE_API_KEY=...
-GOOGLE_VERTEX_TEST_CREDENTIALS=...
 GOOGLE_CLOUD_PROJECT_ID=...
-GOOGLE_CLOUD_LOCATION=...
 GOOGLE_TEST_CREDENTIALS=...
-
-# Grok
-GROK_API_KEY=...
-
-# Groq
-GROQ_API_KEY=...
-
-# Heygen
-HEYGEN_API_KEY=...
+GOOGLE_VERTEX_TEST_CREDENTIALS=...
 
 # Hume
 HUME_API_KEY=...
-HUME_VOICE_ID=...
-
-# Inworld
-INWORLD_API_KEY=...
-
-# Krisp
-KRISP_MODEL_PATH=...
-
-# Krisp Viva
-KRISP_VIVA_MODEL_PATH=...
-
-# LiveKit
-LIVEKIT_API_KEY=...
-LIVEKIT_API_SECRET=...
 
 # LMNT
 LMNT_API_KEY=...
 LMNT_VOICE_ID=...
 
-# MiniMax
-MINIMAX_API_KEY=...
-MINIMAX_GROUP_ID=...
+# Perplexity
+PERPLEXITY_API_KEY=...
 
-# Mistral
-MISTRAL_API_KEY=...
-
-# Neuphonic
-NEUPHONIC_API_KEY=...
-
-# NVIDIA
-NVIDIA_API_KEY=...
+# PlayHT
+PLAYHT_USER_ID=...
+PLAYHT_API_KEY=...
 
 # OpenAI
 OPENAI_API_KEY=...
@@ -121,73 +78,92 @@ OPENAI_API_KEY=...
 # OpenPipe
 OPENPIPE_API_KEY=...
 
-# OpenRouter
-OPENROUTER_API_KEY=...
-
-# Perplexity
-PERPLEXITY_API_KEY=...
-
-# Picovoice Koala
-KOALA_ACCESS_KEY=...
-
-# Piper
-PIPER_BASE_URL=...
-
-# PlayHT
-PLAYHT_USER_ID=...
-PLAYHT_API_KEY=...
-
-# Plivo
-PLIVO_AUTH_ID=...
-PLIVO_AUTH_TOKEN=...
-
-# Qwen
-QWEN_API_KEY=...
-
-# Rime
-RIME_API_KEY=...
-RIME_VOICE_ID=...
-
-# SambaNova
-SAMBANOVA_API_KEY=...
-
-# Sarvam AI
-SARVAM_API_KEY=...
-
-# Sentry
-SENTRY_DSN=...
+# Tavus
+TAVUS_API_KEY=...
+TAVUS_REPLICA_ID=...
+TAVUS_PERSONA_ID=...
 
 # Simli
 SIMLI_API_KEY=...
 SIMLI_FACE_ID=...
 
-# Smart turn
-LOCAL_SMART_TURN_MODEL_PATH=...
-FAL_SMART_TURN_API_KEY=...
+# Krisp
+KRISP_MODEL_PATH=...
 
-# Soniox
-SONIOX_API_KEY=...
+# Krisp Viva
+KRISP_VIVA_MODEL_PATH=...
 
-# Speechmatics
-SPEECHMATICS_API_KEY=...
+# DeepSeek
+DEEPSEEK_API_KEY=...
 
-# Tavus
-TAVUS_API_KEY=...
-TAVUS_REPLICA_ID=...
+# Groq
+GROQ_API_KEY=...
 
-# Telnyx
-TELNYX_API_KEY=...
-TELNYX_ACCOUNT_SID=...
+# Grok
+GROK_API_KEY=...
+
+# Inworld
+INWORLD_API_KEY=...
 
 # Together.ai
 TOGETHER_API_KEY=...
 
+# Cerebras
+CEREBRAS_API_KEY=...
+
+# Fish Audio
+FISH_API_KEY=...
+
+# Assembly AI
+ASSEMBLYAI_API_KEY=...
+
+# OpenRouter
+OPENROUTER_API_KEY=...
+
+# Piper
+PIPER_BASE_URL=...
+
+# Smart turn
+LOCAL_SMART_TURN_MODEL_PATH=...
+FAL_SMART_TURN_API_KEY=...
+
 # Twilio
 TWILIO_ACCOUNT_SID=...
 TWILIO_AUTH_TOKEN=...
 
+# MiniMax
+MINIMAX_API_KEY=...
+MINIMAX_GROUP_ID=...
+
+# Sarvam AI
+SARVAM_API_KEY=...
+
+# Soniox
+SONIOX_API_KEY=
+
+# Speechmatics
+SPEECHMATICS_API_KEY=...
+
+# SambaNova
+SAMBANOVA_API_KEY=...
+
+# Sentry
+SENTRY_DSN=...
+
+# Heygen
+HEYGEN_API_KEY=...
+
+# Mistral
+MISTRAL_API_KEY=...
+
+# NVIDIA
+NVIDIA_API_KEY=...
+
+# Qwen
+QWEN_API_KEY=...
+
 # WhatsApp
-WHATSAPP_TOKEN=...
-WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
-WHATSAPP_PHONE_NUMBER_ID=...
-WHATSAPP_APP_SECRET=...
+WHATSAPP_TOKEN=
+WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=
+WHATSAPP_PHONE_NUMBER_ID=
+WHATSAPP_APP_SECRET=

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

@@ -67,8 +67,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
+        params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
     )
 
     messages = [

examples/foundational/08-bots-arguing.py

@@ -0,0 +1,147 @@
+import asyncio
+import logging
+import os
+from typing import Tuple
+
+import aiohttp
+from dotenv import load_dotenv
+
+from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMContextFrame, TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.processors.aggregators import SentenceAggregator
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.runner.daily import configure
+from pipecat.services.azure import AzureLLMService, AzureTTSService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.fal import FalImageGenService
+from pipecat.transports.daily.transport import DailyTransport
+
+load_dotenv(override=True)
+
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logger = logging.getLogger("pipecat")
+logger.setLevel(logging.DEBUG)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            duration_minutes=10,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=True,
+            camera_width=1024,
+            camera_height=1024,
+        )
+
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
+        tts1 = AzureTTSService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
+        tts2 = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id="jBpfuIE2acCO8z3wKNLl",
+        )
+        dalle = FalImageGenService(
+            params=FalImageGenService.InputParams(image_size="1024x1024"),
+            aiohttp_session=session,
+            key=os.getenv("FAL_KEY"),
+        )
+
+        bot1_messages = [
+            {
+                "role": "system",
+                "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long.",
+            },
+        ]
+        bot2_messages = [
+            {
+                "role": "system",
+                "content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich.",
+            },
+        ]
+
+        async def get_text_and_audio(messages) -> Tuple[str, bytearray]:
+            """This function streams text from the LLM and uses the TTS service to convert
+            that text to speech as it's received.
+            """
+            source_queue = asyncio.Queue()
+            sink_queue = asyncio.Queue()
+            sentence_aggregator = SentenceAggregator()
+            pipeline = Pipeline([llm, sentence_aggregator, tts1], source_queue, sink_queue)
+
+            await source_queue.put(LLMContextFrame(LLMContext(messages)))
+            await source_queue.put(EndFrame())
+            await pipeline.run_pipeline()
+
+            message = ""
+            all_audio = bytearray()
+            while sink_queue.qsize():
+                frame = sink_queue.get_nowait()
+                if isinstance(frame, TextFrame):
+                    message += frame.text
+                elif isinstance(frame, AudioFrame):
+                    all_audio.extend(frame.audio)
+
+            return (message, all_audio)
+
+        async def get_bot1_statement():
+            message, audio = await get_text_and_audio(bot1_messages)
+
+            bot1_messages.append({"role": "assistant", "content": message})
+            bot2_messages.append({"role": "user", "content": message})
+
+            return audio
+
+        async def get_bot2_statement():
+            message, audio = await get_text_and_audio(bot2_messages)
+
+            bot2_messages.append({"role": "assistant", "content": message})
+            bot1_messages.append({"role": "user", "content": message})
+
+            return audio
+
+        async def argue():
+            for i in range(100):
+                print(f"In iteration {i}")
+
+                bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
+
+                (audio1, image_data1) = await asyncio.gather(
+                    get_bot1_statement(), dalle.run_image_gen(bot1_description)
+                )
+                await transport.send_queue.put(
+                    [
+                        ImageFrame(image_data1[1], image_data1[2]),
+                        AudioFrame(audio1),
+                    ]
+                )
+
+                bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
+
+                (audio2, image_data2) = await asyncio.gather(
+                    get_bot2_statement(), dalle.run_image_gen(bot2_description)
+                )
+                await transport.send_queue.put(
+                    [
+                        ImageFrame(image_data2[1], image_data2[2]),
+                        AudioFrame(audio2),
+                    ]
+                )
+
+        await asyncio.gather(transport.run(), argue())
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -1,170 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import io
-import os
-import re
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import (
-    Frame,
-    LLMRunFrame,
-    MetricsFrame,
-)
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-
-load_dotenv(override=True)
-
-
-def format_metrics(metrics, indent=0):
-    lines = []
-    tab = "\t" * indent
-
-    for metric in metrics:
-        lines.append(tab + type(metric).__name__)
-        for field, value in vars(metric).items():
-            if hasattr(value, "__dict__") and not isinstance(
-                value, (str, int, float, bool, type(None))
-            ):
-                lines.append(f"{tab}\t{field}={type(value).__name__}")
-                for k, v in vars(value).items():
-                    lines.append(f"{tab}\t\t{k}={repr(v)}")
-            else:
-                lines.append(f"{tab}\t{field}={repr(value)}")
-
-    return "\n".join(lines)
-
-
-class MetricsFrameLogger(FrameProcessor):
-    """MetricsFrameLogger formats and logs all MetericsFrames"""
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, MetricsFrame):
-            logger.info(f"{frame.name}\n    {format_metrics(frame.data)}")
-            await self.push_frame(frame, direction)
-
-        # ALWAYS push all frames
-        else:
-            # SUPER IMPORTANT: always push every frame!
-            await self.push_frame(frame, direction)
-
-
-# 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(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        video_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-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"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    metrics_frame_processor = MetricsFrameLogger()
-
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            stt,
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-            metrics_frame_processor,  # pretty print metrics frames
-        ]
-    )
-
-    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: {client}")
-        # Kick off the conversation.
-        messages.append({"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()
-
-    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/14r-function-calling-aws.py

@@ -79,8 +79,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = AWSBedrockLLMService(
         aws_region="us-west-2",
-        model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
-        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
+        params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
     )
 
     # You can also register a function_name of None to get all functions

pyproject.toml

@@ -38,6 +38,7 @@ dependencies = [
     # Pinning numba to resolve package dependencies
     "numba==0.61.2",
     "wait_for2>=0.4.1; python_version<'3.12'",
+    "pipecat-ai-cli"
 ]
 
 [project.urls]

src/pipecat/processors/aggregators/llm_context.py

@@ -106,19 +106,6 @@ class LLMContext:
         self._tools: ToolsSchema | NotGiven = LLMContext._normalize_and_validate_tools(tools)
         self._tool_choice: LLMContextToolChoice | NotGiven = tool_choice
 
-    @property
-    def messages(self) -> List[LLMContextMessage]:
-        """Get the current messages list.
-
-        NOTE: This is equivalent to calling `get_messages()` with no filter. If
-        you want to filter out LLM-specific messages that don't pertain to your
-        LLM, use `get_messages()` directly.
-
-        Returns:
-            List of conversation messages.
-        """
-        return self.get_messages()
-
     def get_messages(self, llm_specific_filter: Optional[str] = None) -> List[LLMContextMessage]:
         """Get the current messages list.
 
@@ -126,8 +113,7 @@ class LLMContext:
             llm_specific_filter: Optional filter to return LLM-specific
                 messages for the given LLM, in addition to the standard
                 messages. If messages end up being filtered, an error will be
-                logged; this is intended to catch accidental use of
-                incompatible LLM-specific messages.
+                logged.
 
         Returns:
             List of conversation messages.

src/pipecat/processors/frameworks/rtvi.py

@@ -1018,7 +1018,6 @@ class RTVIObserver(BaseObserver):
 
         if (
             isinstance(frame, (UserStartedSpeakingFrame, UserStoppedSpeakingFrame))
-            and (direction == FrameDirection.DOWNSTREAM)
             and self._params.user_speaking_enabled
         ):
             await self._handle_interruptions(frame)

src/pipecat/runner/daily.py

@@ -76,7 +76,6 @@ class DailyRoomConfig(BaseModel):
 async def configure(
     aiohttp_session: aiohttp.ClientSession,
     *,
-    api_key: Optional[str] = None,
     room_exp_duration: Optional[float] = 2.0,
     token_exp_duration: Optional[float] = 2.0,
     sip_caller_phone: Optional[str] = None,
@@ -93,7 +92,6 @@ async def configure(
 
     Args:
         aiohttp_session: HTTP session for making API requests.
-        api_key: Daily API key.
         room_exp_duration: Room expiration time in hours.
         token_exp_duration: Token expiration time in hours.
         sip_caller_phone: Phone number or identifier for SIP display name.
@@ -131,7 +129,7 @@ async def configure(
         config = await configure(session, room_properties=custom_props)
     """
     # Check for required API key
-    api_key = api_key or os.getenv("DAILY_API_KEY")
+    api_key = os.getenv("DAILY_API_KEY")
     if not api_key:
         raise Exception(
             "DAILY_API_KEY environment variable is required. "

src/pipecat/runner/run.py

@@ -82,7 +82,6 @@ from loguru import logger
 
 from pipecat.runner.types import (
     DailyRunnerArguments,
-    RunnerArguments,
     SmallWebRTCRunnerArguments,
     WebSocketRunnerArguments,
 )
@@ -310,7 +309,7 @@ def _setup_webrtc_routes(
     ):
         """Mimic Pipecat Cloud's proxy."""
         active_session = active_sessions.get(session_id)
-        if active_session is None:
+        if not active_session:
             return Response(content="Invalid or not-yet-ready session_id", status_code=404)
 
         if path.endswith("api/offer"):
@@ -530,9 +529,9 @@ def _setup_daily_routes(app: FastAPI):
     """Set up Daily-specific routes."""
 
     @app.get("/")
-    async def create_room_and_start_agent():
+    async def start_agent():
         """Launch a Daily bot and redirect to room."""
-        print("Starting bot with Daily transport and redirecting to Daily room")
+        print("Starting bot with Daily transport")
 
         import aiohttp
 
@@ -547,11 +546,11 @@ def _setup_daily_routes(app: FastAPI):
             asyncio.create_task(bot_module.bot(runner_args))
             return RedirectResponse(room_url)
 
-    @app.post("/start")
-    async def start_agent(request: Request):
-        """Handler for /start endpoints.
+    async def _handle_rtvi_request(request: Request):
+        """Common handler for both /start and /connect endpoints.
 
         Expects POST body like::
+
             {
                 "createDailyRoom": true,
                 "dailyRoomProperties": { "start_video_off": true },
@@ -568,32 +567,45 @@ def _setup_daily_routes(app: FastAPI):
             logger.error(f"Failed to parse request body: {e}")
             request_data = {}
 
-        create_daily_room = request_data.get("createDailyRoom", False)
-        body = request_data.get("body", {})
+        # Extract the body data that should be passed to the bot
+        # This mimics Pipecat Cloud's behavior
+        bot_body = request_data.get("body", {})
 
-        bot_module = _get_bot_module()
-
-        result = None
-        if create_daily_room:
-            import aiohttp
-
-            from pipecat.runner.daily import configure
-
-            async with aiohttp.ClientSession() as session:
-                room_url, token = await configure(session)
-                runner_args = DailyRunnerArguments(room_url=room_url, token=token, body=body)
-                result = {
-                    "dailyRoom": room_url,
-                    "dailyToken": token,
-                    "sessionId": str(uuid.uuid4()),
-                }
+        # Log the extracted body data for debugging
+        if bot_body:
+            logger.info(f"Extracted body data for bot: {bot_body}")
         else:
-            runner_args = RunnerArguments(body=body)
+            logger.debug("No body data provided in request")
 
-        # Start the bot in the background
-        asyncio.create_task(bot_module.bot(runner_args))
+        from pipecat.runner.daily import configure
 
-        return result
+        async with aiohttp.ClientSession() as session:
+            room_url, token = await configure(session)
+
+            # Start the bot in the background with extracted body data
+            bot_module = _get_bot_module()
+            runner_args = DailyRunnerArguments(room_url=room_url, token=token, body=bot_body)
+            asyncio.create_task(bot_module.bot(runner_args))
+            # Match PCC /start endpoint response format:
+            return {"dailyRoom": room_url, "dailyToken": token}
+
+    @app.post("/start")
+    async def rtvi_start(request: Request):
+        """Launch a Daily bot and return connection info for RTVI clients."""
+        return await _handle_rtvi_request(request)
+
+    @app.post("/connect")
+    async def rtvi_connect(request: Request):
+        """Launch a Daily bot and return connection info for RTVI clients.
+
+        .. deprecated:: 0.0.78
+            Use /start instead. This endpoint will be removed in a future version.
+        """
+        logger.warning(
+            "DEPRECATED: /connect endpoint is deprecated. Please use /start instead. "
+            "This endpoint will be removed in a future version."
+        )
+        return await _handle_rtvi_request(request)
 
 
 def _setup_telephony_routes(app: FastAPI, *, transport_type: str, proxy: str):

src/pipecat/runner/types.py

@@ -20,11 +20,9 @@ from fastapi import WebSocket
 class RunnerArguments:
     """Base class for runner session arguments."""
 
-    # Use kw_only so subclasses don't need to worry about ordering.
-    handle_sigint: bool = field(init=False, kw_only=True)
-    handle_sigterm: bool = field(init=False, kw_only=True)
-    pipeline_idle_timeout_secs: int = field(init=False, kw_only=True)
-    body: Optional[Any] = field(default_factory=dict, kw_only=True)
+    handle_sigint: bool = field(init=False)
+    handle_sigterm: bool = field(init=False)
+    pipeline_idle_timeout_secs: int = field(init=False)
 
     def __post_init__(self):
         self.handle_sigint = False
@@ -44,6 +42,7 @@ class DailyRunnerArguments(RunnerArguments):
 
     room_url: str
     token: Optional[str] = None
+    body: Optional[Any] = field(default_factory=dict)
 
 
 @dataclass
@@ -56,6 +55,7 @@ class WebSocketRunnerArguments(RunnerArguments):
     """
 
     websocket: WebSocket
+    body: Optional[Any] = field(default_factory=dict)
 
 
 @dataclass

src/pipecat/services/aws/llm.py

@@ -720,11 +720,11 @@ class AWSBedrockLLMService(LLMService):
             additional_model_request_fields: Additional model-specific parameters.
         """
 
-        max_tokens: Optional[int] = Field(default=None, ge=1)
-        temperature: Optional[float] = Field(default=None, ge=0.0, le=1.0)
-        top_p: Optional[float] = Field(default=None, ge=0.0, le=1.0)
+        max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
+        temperature: Optional[float] = Field(default_factory=lambda: 0.7, ge=0.0, le=1.0)
+        top_p: Optional[float] = Field(default_factory=lambda: 0.999, ge=0.0, le=1.0)
         stop_sequences: Optional[List[str]] = Field(default_factory=lambda: [])
-        latency: Optional[str] = Field(default=None)
+        latency: Optional[str] = Field(default_factory=lambda: "standard")
         additional_model_request_fields: Optional[Dict[str, Any]] = Field(default_factory=dict)
 
     def __init__(
@@ -801,24 +801,6 @@ class AWSBedrockLLMService(LLMService):
         """
         return True
 
-    def _build_inference_config(self) -> Dict[str, Any]:
-        """Build inference config with only the parameters that are set.
-
-        This prevents conflicts with models (e.g., Claude Sonnet 4.5) that don't
-        allow certain parameter combinations like temperature and top_p together.
-
-        Returns:
-            Dictionary containing only the inference parameters that are not None.
-        """
-        inference_config = {}
-        if self._settings["max_tokens"] is not None:
-            inference_config["maxTokens"] = self._settings["max_tokens"]
-        if self._settings["temperature"] is not None:
-            inference_config["temperature"] = self._settings["temperature"]
-        if self._settings["top_p"] is not None:
-            inference_config["topP"] = self._settings["top_p"]
-        return inference_config
-
     async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
@@ -844,16 +826,16 @@ class AWSBedrockLLMService(LLMService):
         model_id = self.model_name
 
         # Prepare request parameters
-        inference_config = self._build_inference_config()
-
         request_params = {
             "modelId": model_id,
             "messages": messages,
+            "inferenceConfig": {
+                "maxTokens": 8192,
+                "temperature": 0.7,
+                "topP": 0.9,
+            },
         }
 
-        if inference_config:
-            request_params["inferenceConfig"] = inference_config
-
         if system:
             request_params["system"] = system
 
@@ -992,20 +974,21 @@ class AWSBedrockLLMService(LLMService):
             tools = params_from_context["tools"]
             tool_choice = params_from_context["tool_choice"]
 
-            # Set up inference config - only include parameters that are set
-            inference_config = self._build_inference_config()
+            # Set up inference config
+            inference_config = {
+                "maxTokens": self._settings["max_tokens"],
+                "temperature": self._settings["temperature"],
+                "topP": self._settings["top_p"],
+            }
 
             # Prepare request parameters
             request_params = {
                 "modelId": self.model_name,
                 "messages": messages,
+                "inferenceConfig": inference_config,
                 "additionalModelRequestFields": self._settings["additional_model_request_fields"],
             }
 
-            # Only add inference config if it has parameters
-            if inference_config:
-                request_params["inferenceConfig"] = inference_config
-
             # Add system message
             if system:
                 request_params["system"] = system

src/pipecat/services/aws/nova_sonic/context.py

@@ -10,8 +10,7 @@ This module provides specialized context aggregators and message handling for AW
 including conversation history management and role-specific message processing.
 
 .. deprecated:: 0.0.91
-    AWS Nova Sonic no longer uses types from this module under the hood.
-    It now uses `LLMContext` and `LLMContextAggregatorPair`.
+    AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`.
     Using the new patterns should allow you to not need types from this module.
 
     BEFORE:
@@ -27,6 +26,9 @@ including conversation history management and role-specific message processing.
     context: AWSNovaSonicLLMContext
     # or
     context: OpenAILLMContext
+
+    # Reading messages from context
+    messages = context.messages
     ```
 
     AFTER:
@@ -40,6 +42,9 @@ including conversation history management and role-specific message processing.
 
     # Context type
     context: LLMContext
+
+    # Reading messages from context
+    messages = context.get_messages()
     ```
 """
 
@@ -48,10 +53,8 @@ import warnings
 with warnings.catch_warnings():
     warnings.simplefilter("always")
     warnings.warn(
-        "Types in pipecat.services.aws.nova_sonic.context (or "
-        "pipecat.services.aws_nova_sonic.context) are deprecated. \n"
-        "AWS Nova Sonic no longer uses types from this module under the hood. \n"
-        "It now uses `LLMContext` and `LLMContextAggregatorPair`. \n"
+        "Types in pipecat.services.aws.nova_sonic.context are deprecated. \n"
+        "AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`. \n"
         "Using the new patterns should allow you to not need types from this module.\n\n"
         "BEFORE:\n"
         "```\n"
@@ -64,6 +67,8 @@ with warnings.catch_warnings():
         "context: AWSNovaSonicLLMContext\n"
         "# or\n"
         "context: OpenAILLMContext\n\n"
+        "# Reading messages from context\n"
+        "messages = context.messages\n"
         "```\n\n"
         "AFTER:\n"
         "```\n"
@@ -74,363 +79,9 @@ with warnings.catch_warnings():
         "frame: LLMContextFrame\n\n"
         "# Context type\n"
         "context: LLMContext\n\n"
+        "# Reading messages from context\n"
+        "messages = context.messages\n"
         "```",
         DeprecationWarning,
         stacklevel=2,
     )
-
-import copy
-from dataclasses import dataclass, field
-from enum import Enum
-
-from loguru import logger
-
-from pipecat.frames.frames import (
-    BotStoppedSpeakingFrame,
-    DataFrame,
-    Frame,
-    FunctionCallResultFrame,
-    InterruptionFrame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMMessagesAppendFrame,
-    LLMMessagesUpdateFrame,
-    LLMSetToolChoiceFrame,
-    LLMSetToolsFrame,
-    TextFrame,
-    UserImageRawFrame,
-)
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.aws.nova_sonic.frames import AWSNovaSonicFunctionCallResultFrame
-from pipecat.services.openai.llm import (
-    OpenAIAssistantContextAggregator,
-    OpenAIUserContextAggregator,
-)
-
-
-class Role(Enum):
-    """Roles supported in AWS Nova Sonic conversations.
-
-    Parameters:
-        SYSTEM: System-level messages (not used in conversation history).
-        USER: Messages sent by the user.
-        ASSISTANT: Messages sent by the assistant.
-        TOOL: Messages sent by tools (not used in conversation history).
-    """
-
-    SYSTEM = "SYSTEM"
-    USER = "USER"
-    ASSISTANT = "ASSISTANT"
-    TOOL = "TOOL"
-
-
-@dataclass
-class AWSNovaSonicConversationHistoryMessage:
-    """A single message in AWS Nova Sonic conversation history.
-
-    Parameters:
-        role: The role of the message sender (USER or ASSISTANT only).
-        text: The text content of the message.
-    """
-
-    role: Role  # only USER and ASSISTANT
-    text: str
-
-
-@dataclass
-class AWSNovaSonicConversationHistory:
-    """Complete conversation history for AWS Nova Sonic initialization.
-
-    Parameters:
-        system_instruction: System-level instruction for the conversation.
-        messages: List of conversation messages between user and assistant.
-    """
-
-    system_instruction: str = None
-    messages: list[AWSNovaSonicConversationHistoryMessage] = field(default_factory=list)
-
-
-class AWSNovaSonicLLMContext(OpenAILLMContext):
-    """Specialized LLM context for AWS Nova Sonic service.
-
-    Extends OpenAI context with Nova Sonic-specific message handling,
-    conversation history management, and text buffering capabilities.
-    """
-
-    def __init__(self, messages=None, tools=None, **kwargs):
-        """Initialize AWS Nova Sonic LLM context.
-
-        Args:
-            messages: Initial messages for the context.
-            tools: Available tools for the context.
-            **kwargs: Additional arguments passed to parent class.
-        """
-        super().__init__(messages=messages, tools=tools, **kwargs)
-        self.__setup_local()
-
-    def __setup_local(self, system_instruction: str = ""):
-        self._assistant_text = ""
-        self._user_text = ""
-        self._system_instruction = system_instruction
-
-    @staticmethod
-    def upgrade_to_nova_sonic(
-        obj: OpenAILLMContext, system_instruction: str
-    ) -> "AWSNovaSonicLLMContext":
-        """Upgrade an OpenAI context to AWS Nova Sonic context.
-
-        Args:
-            obj: The OpenAI context to upgrade.
-            system_instruction: System instruction for the context.
-
-        Returns:
-            The upgraded AWS Nova Sonic context.
-        """
-        if isinstance(obj, OpenAILLMContext) and not isinstance(obj, AWSNovaSonicLLMContext):
-            obj.__class__ = AWSNovaSonicLLMContext
-            obj.__setup_local(system_instruction)
-        return obj
-
-    # NOTE: this method has the side-effect of updating _system_instruction from messages
-    def get_messages_for_initializing_history(self) -> AWSNovaSonicConversationHistory:
-        """Get conversation history for initializing AWS Nova Sonic session.
-
-        Processes stored messages and extracts system instruction and conversation
-        history in the format expected by AWS Nova Sonic.
-
-        Returns:
-            Formatted conversation history with system instruction and messages.
-        """
-        history = AWSNovaSonicConversationHistory(system_instruction=self._system_instruction)
-
-        # Bail if there are no messages
-        if not self.messages:
-            return history
-
-        messages = copy.deepcopy(self.messages)
-
-        # If we have a "system" message as our first message, let's pull that out into "instruction"
-        if messages[0].get("role") == "system":
-            system = messages.pop(0)
-            content = system.get("content")
-            if isinstance(content, str):
-                history.system_instruction = content
-            elif isinstance(content, list):
-                history.system_instruction = content[0].get("text")
-            if history.system_instruction:
-                self._system_instruction = history.system_instruction
-
-        # Process remaining messages to fill out conversation history.
-        # Nova Sonic supports "user" and "assistant" messages in history.
-        for message in messages:
-            history_message = self.from_standard_message(message)
-            if history_message:
-                history.messages.append(history_message)
-
-        return history
-
-    def get_messages_for_persistent_storage(self):
-        """Get messages formatted for persistent storage.
-
-        Returns:
-            List of messages including system instruction if present.
-        """
-        messages = super().get_messages_for_persistent_storage()
-        # If we have a system instruction and messages doesn't already contain it, add it
-        if self._system_instruction and not (messages and messages[0].get("role") == "system"):
-            messages.insert(0, {"role": "system", "content": self._system_instruction})
-        return messages
-
-    def from_standard_message(self, message) -> AWSNovaSonicConversationHistoryMessage:
-        """Convert standard message format to Nova Sonic format.
-
-        Args:
-            message: Standard message dictionary to convert.
-
-        Returns:
-            Nova Sonic conversation history message, or None if not convertible.
-        """
-        role = message.get("role")
-        if message.get("role") == "user" or message.get("role") == "assistant":
-            content = message.get("content")
-            if isinstance(message.get("content"), list):
-                content = ""
-                for c in message.get("content"):
-                    if c.get("type") == "text":
-                        content += " " + c.get("text")
-                    else:
-                        logger.error(
-                            f"Unhandled content type in context message: {c.get('type')} - {message}"
-                        )
-            # There won't be content if this is an assistant tool call entry.
-            # We're ignoring those since they can't be loaded into AWS Nova Sonic conversation
-            # history
-            if content:
-                return AWSNovaSonicConversationHistoryMessage(role=Role[role.upper()], text=content)
-        # NOTE: we're ignoring messages with role "tool" since they can't be loaded into AWS Nova
-        # Sonic conversation history
-
-    def buffer_user_text(self, text):
-        """Buffer user text for later flushing to context.
-
-        Args:
-            text: User text to buffer.
-        """
-        self._user_text += f" {text}" if self._user_text else text
-        # logger.debug(f"User text buffered: {self._user_text}")
-
-    def flush_aggregated_user_text(self) -> str:
-        """Flush buffered user text to context as a complete message.
-
-        Returns:
-            The flushed user text, or empty string if no text was buffered.
-        """
-        if not self._user_text:
-            return ""
-        user_text = self._user_text
-        message = {
-            "role": "user",
-            "content": [{"type": "text", "text": user_text}],
-        }
-        self._user_text = ""
-        self.add_message(message)
-        # logger.debug(f"Context updated (user): {self.get_messages_for_logging()}")
-        return user_text
-
-    def buffer_assistant_text(self, text):
-        """Buffer assistant text for later flushing to context.
-
-        Args:
-            text: Assistant text to buffer.
-        """
-        self._assistant_text += text
-        # logger.debug(f"Assistant text buffered: {self._assistant_text}")
-
-    def flush_aggregated_assistant_text(self):
-        """Flush buffered assistant text to context as a complete message."""
-        if not self._assistant_text:
-            return
-        message = {
-            "role": "assistant",
-            "content": [{"type": "text", "text": self._assistant_text}],
-        }
-        self._assistant_text = ""
-        self.add_message(message)
-        # logger.debug(f"Context updated (assistant): {self.get_messages_for_logging()}")
-
-
-@dataclass
-class AWSNovaSonicMessagesUpdateFrame(DataFrame):
-    """Frame containing updated AWS Nova Sonic context.
-
-    Parameters:
-        context: The updated AWS Nova Sonic LLM context.
-    """
-
-    context: AWSNovaSonicLLMContext
-
-
-class AWSNovaSonicUserContextAggregator(OpenAIUserContextAggregator):
-    """Context aggregator for user messages in AWS Nova Sonic conversations.
-
-    Extends the OpenAI user context aggregator to emit Nova Sonic-specific
-    context update frames.
-    """
-
-    async def process_frame(
-        self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM
-    ):
-        """Process frames and emit Nova Sonic-specific context updates.
-
-        Args:
-            frame: The frame to process.
-            direction: The direction the frame is traveling.
-        """
-        await super().process_frame(frame, direction)
-
-        # Parent does not push LLMMessagesUpdateFrame
-        if isinstance(frame, LLMMessagesUpdateFrame):
-            await self.push_frame(AWSNovaSonicMessagesUpdateFrame(context=self._context))
-
-
-class AWSNovaSonicAssistantContextAggregator(OpenAIAssistantContextAggregator):
-    """Context aggregator for assistant messages in AWS Nova Sonic conversations.
-
-    Provides specialized handling for assistant responses and function calls
-    in AWS Nova Sonic context, with custom frame processing logic.
-    """
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process frames with Nova Sonic-specific logic.
-
-        Args:
-            frame: The frame to process.
-            direction: The direction the frame is traveling.
-        """
-        # HACK: For now, disable the context aggregator by making it just pass through all frames
-        # that the parent handles (except the function call stuff, which we still need).
-        # For an explanation of this hack, see
-        # AWSNovaSonicLLMService._report_assistant_response_text_added.
-        if isinstance(
-            frame,
-            (
-                InterruptionFrame,
-                LLMFullResponseStartFrame,
-                LLMFullResponseEndFrame,
-                TextFrame,
-                LLMMessagesAppendFrame,
-                LLMMessagesUpdateFrame,
-                LLMSetToolsFrame,
-                LLMSetToolChoiceFrame,
-                UserImageRawFrame,
-                BotStoppedSpeakingFrame,
-            ),
-        ):
-            await self.push_frame(frame, direction)
-        else:
-            await super().process_frame(frame, direction)
-
-    async def handle_function_call_result(self, frame: FunctionCallResultFrame):
-        """Handle function call results for AWS Nova Sonic.
-
-        Args:
-            frame: The function call result frame to handle.
-        """
-        await super().handle_function_call_result(frame)
-
-        # The standard function callback code path pushes the FunctionCallResultFrame from the LLM
-        # itself, so we didn't have a chance to add the result to the AWS Nova Sonic server-side
-        # context. Let's push a special frame to do that.
-        await self.push_frame(
-            AWSNovaSonicFunctionCallResultFrame(result_frame=frame), FrameDirection.UPSTREAM
-        )
-
-
-@dataclass
-class AWSNovaSonicContextAggregatorPair:
-    """Pair of user and assistant context aggregators for AWS Nova Sonic.
-
-    Parameters:
-        _user: The user context aggregator.
-        _assistant: The assistant context aggregator.
-    """
-
-    _user: AWSNovaSonicUserContextAggregator
-    _assistant: AWSNovaSonicAssistantContextAggregator
-
-    def user(self) -> AWSNovaSonicUserContextAggregator:
-        """Get the user context aggregator.
-
-        Returns:
-            The user context aggregator instance.
-        """
-        return self._user
-
-    def assistant(self) -> AWSNovaSonicAssistantContextAggregator:
-        """Get the assistant context aggregator.
-
-        Returns:
-            The assistant context aggregator instance.
-        """
-        return self._assistant

src/pipecat/services/aws_nova_sonic/context.py

@@ -10,12 +10,78 @@ This module provides specialized context aggregators and message handling for AW
 including conversation history management and role-specific message processing.
 
 .. deprecated:: 0.0.91
-    AWS Nova Sonic no longer uses types from this module under the hood.
-    It now uses `LLMContext` and `LLMContextAggregatorPair`.
+    AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`.
     Using the new patterns should allow you to not need types from this module.
 
-    See deprecation warning in pipecat.services.aws.nova_sonic.context for more
-    details.
+    BEFORE:
+    ```
+    # Setup
+    context = OpenAILLMContext(messages, tools)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    # Context frame type
+    frame: OpenAILLMContextFrame
+
+    # Context type
+    context: AWSNovaSonicLLMContext
+    # or
+    context: OpenAILLMContext
+
+    # Reading messages from context
+    messages = context.messages
+    ```
+
+    AFTER:
+    ```
+    # Setup
+    context = LLMContext(messages, tools)
+    context_aggregator = LLMContextAggregatorPair(context)
+
+    # Context frame type
+    frame: LLMContextFrame
+
+    # Context type
+    context: LLMContext
+
+    # Reading messages from context
+    messages = context.get_messages()
+    ```
 """
 
-from pipecat.services.aws.nova_sonic.context import *
+import warnings
+
+with warnings.catch_warnings():
+    warnings.simplefilter("always")
+    warnings.warn(
+        "Types in pipecat.services.aws_nova_sonic.context are deprecated. \n"
+        "AWS Nova Sonic now supports `LLMContext` and `LLMContextAggregatorPair`. \n"
+        "Using the new patterns should allow you to not need types from this module.\n\n"
+        "BEFORE:\n"
+        "```\n"
+        "# Setup\n"
+        "context = OpenAILLMContext(messages, tools)\n"
+        "context_aggregator = llm.create_context_aggregator(context)\n\n"
+        "# Context frame type\n"
+        "frame: OpenAILLMContextFrame\n\n"
+        "# Context type\n"
+        "context: AWSNovaSonicLLMContext\n"
+        "# or\n"
+        "context: OpenAILLMContext\n\n"
+        "# Reading messages from context\n"
+        "messages = context.messages\n"
+        "```\n\n"
+        "AFTER:\n"
+        "```\n"
+        "# Setup\n"
+        "context = LLMContext(messages, tools)\n"
+        "context_aggregator = LLMContextAggregatorPair(context)\n\n"
+        "# Context frame type\n"
+        "frame: LLMContextFrame\n\n"
+        "# Context type\n"
+        "context: LLMContext\n\n"
+        "# Reading messages from context\n"
+        "messages = context.messages\n"
+        "```",
+        DeprecationWarning,
+        stacklevel=2,
+    )

src/pipecat/utils/string.py

@@ -47,7 +47,6 @@ SENTENCE_ENDING_PUNCTUATION: FrozenSet[str] = frozenset(
         "!",
         "?",
         ";",
-        "…",
         # East Asian punctuation (Chinese (Traditional & Simplified), Japanese, Korean)
         "。",  # Ideographic full stop
         "？",  # Full-width question mark

uv.lock

@@ -2819,6 +2819,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2d/00/d90b10b962b4277f5e64a78b6609968859ff86889f5b898c1a778c06ec00/lark-1.2.2-py3-none-any.whl", hash = "sha256:c2276486b02f0f1b90be155f2c8ba4a8e194d42775786db622faccd652d8e80c", size = 111036, upload-time = "2024-08-13T19:48:58.603Z" },
 ]
 
+[[package]]
+name = "linkify-it-py"
+version = "2.0.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "uc-micro-py" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2a/ae/bb56c6828e4797ba5a4821eec7c43b8bf40f69cda4d4f5f8c8a2810ec96a/linkify-it-py-2.0.3.tar.gz", hash = "sha256:68cda27e162e9215c17d786649d1da0021a451bdc436ef9e0fa0ba5234b9b048", size = 27946, upload-time = "2024-02-04T14:48:04.179Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/04/1e/b832de447dee8b582cac175871d2f6c3d5077cc56d5575cadba1fd1cccfa/linkify_it_py-2.0.3-py3-none-any.whl", hash = "sha256:6bcbc417b0ac14323382aef5c5192c0075bf8a9d6b41820a2b66371eac6b6d79", size = 19820, upload-time = "2024-02-04T14:48:02.496Z" },
+]
+
 [[package]]
 name = "livekit"
 version = "1.0.13"
@@ -2957,6 +2969,14 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/94/54/e7d793b573f298e1c9013b8c4dade17d481164aa517d1d7148619c2cedbf/markdown_it_py-4.0.0-py3-none-any.whl", hash = "sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147", size = 87321, upload-time = "2025-08-11T12:57:51.923Z" },
 ]
 
+[package.optional-dependencies]
+linkify = [
+    { name = "linkify-it-py" },
+]
+plugins = [
+    { name = "mdit-py-plugins" },
+]
+
 [[package]]
 name = "markupsafe"
 version = "3.0.3"
@@ -3156,6 +3176,18 @@ cli = [
     { name = "typer" },
 ]
 
+[[package]]
+name = "mdit-py-plugins"
+version = "0.5.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b2/fd/a756d36c0bfba5f6e39a1cdbdbfdd448dc02692467d83816dff4592a1ebc/mdit_py_plugins-0.5.0.tar.gz", hash = "sha256:f4918cb50119f50446560513a8e311d574ff6aaed72606ddae6d35716fe809c6", size = 44655, upload-time = "2025-08-11T07:25:49.083Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fb/86/dd6e5db36df29e76c7a7699123569a4a18c1623ce68d826ed96c62643cae/mdit_py_plugins-0.5.0-py3-none-any.whl", hash = "sha256:07a08422fc1936a5d26d146759e9155ea466e842f5ab2f7d2266dd084c8dab1f", size = 57205, upload-time = "2025-08-11T07:25:47.597Z" },
+]
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -4401,6 +4433,7 @@ dependencies = [
     { name = "numpy" },
     { name = "openai" },
     { name = "pillow" },
+    { name = "pipecat-ai-cli" },
     { name = "protobuf" },
     { name = "pydantic" },
     { name = "pyloudnorm" },
@@ -4693,6 +4726,7 @@ requires-dist = [
     { name = "pipecat-ai", extras = ["websockets-base"], marker = "extra == 'sarvam'" },
     { name = "pipecat-ai", extras = ["websockets-base"], marker = "extra == 'soniox'" },
     { name = "pipecat-ai", extras = ["websockets-base"], marker = "extra == 'websocket'" },
+    { name = "pipecat-ai-cli" },
     { name = "pipecat-ai-krisp", marker = "extra == 'krisp'", specifier = "~=0.4.0" },
     { name = "pipecat-ai-small-webrtc-prebuilt", marker = "extra == 'runner'", specifier = ">=1.0.0" },
     { name = "protobuf", specifier = "~=5.29.3" },
@@ -4749,6 +4783,24 @@ docs = [
     { name = "toml" },
 ]
 
+[[package]]
+name = "pipecat-ai-cli"
+version = "0.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jinja2" },
+    { name = "pipecat-ai-tail" },
+    { name = "pipecatcloud" },
+    { name = "questionary" },
+    { name = "rich" },
+    { name = "ruff" },
+    { name = "typer" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/46/44/cf1e357fd6d61932cff89e2b6375b481524a376450cd6894ab1702ffdaf7/pipecat_ai_cli-0.1.0.tar.gz", hash = "sha256:89a43db1bc677bf77d841b17a6aebc382cb4d5fcac5c238642b02c2bdc039428", size = 326329, upload-time = "2025-10-18T00:29:13.999Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/06/41/e239db103275d09cca5263d1f8d251b6f63b3d22b8c45f1e40d54c206f8a/pipecat_ai_cli-0.1.0-py3-none-any.whl", hash = "sha256:fe7ae04583b41ffd0c2363f25899038b2fcbd93818c2dfc87cc9061c5154eb06", size = 64060, upload-time = "2025-10-18T00:29:12.442Z" },
+]
+
 [[package]]
 name = "pipecat-ai-krisp"
 version = "0.4.0"
@@ -4767,6 +4819,41 @@ dependencies = [
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/6d/c8/19e9edb707581431c74e57da386656b9f9072c7a968f5fa49005e0b53cd6/pipecat_ai_small_webrtc_prebuilt-1.0.0.tar.gz", hash = "sha256:7e3d1cba420842d469ee9ecae321a086732392acb2625d5587a54d28a16ca0ea", size = 563883, upload-time = "2025-07-25T17:57:53.266Z" }
 
+[[package]]
+name = "pipecat-ai-tail"
+version = "0.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pipecat-ai" },
+    { name = "textual" },
+    { name = "textual-plotext" },
+    { name = "websockets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ff/0f/f36ffe205e337e40ca910d9945517b7d5d6e7cc3b79f4820aa4a20997a20/pipecat_ai_tail-0.0.1.tar.gz", hash = "sha256:092f7f8f4a660423e032d8f84a3860e0bdaf080590418617f11116b0c1451b6c", size = 1537968, upload-time = "2025-10-02T17:16:52.972Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5d/2c/4f18094ff5362d230ed41f71cec5bfbc2408d27c8b6e75270e321d4ec4ca/pipecat_ai_tail-0.0.1-py3-none-any.whl", hash = "sha256:10f4d5d667757e051ba94081ae78c20b6029753f7559bd9df4e52ed4807026d3", size = 23447, upload-time = "2025-10-02T17:16:51.841Z" },
+]
+
+[[package]]
+name = "pipecatcloud"
+version = "0.2.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohttp" },
+    { name = "fastapi" },
+    { name = "loguru" },
+    { name = "python-dotenv" },
+    { name = "questionary" },
+    { name = "synchronicity" },
+    { name = "toml" },
+    { name = "typer" },
+    { name = "uvicorn" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a0/c4/59ea7895a464c92961fc41f4ab0d879f5fe60ec601407caee31aa6901914/pipecatcloud-0.2.6.tar.gz", hash = "sha256:1b980e4317258f2fa5837652b1db65331300319758b2980259a52bb282a2f084", size = 64698, upload-time = "2025-10-09T18:34:26.488Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/93/2a/830890cd08f57e4e8994f8002f83f991f98582cbb28eb5196c0eda4640a0/pipecatcloud-0.2.6-py3-none-any.whl", hash = "sha256:1017180a1675526c1688e134e192ea03d6f56b4514e3fe5342fcf43c0806eb65", size = 50740, upload-time = "2025-10-09T18:34:25.396Z" },
+]
+
 [[package]]
 name = "platformdirs"
 version = "4.4.0"
@@ -4776,6 +4863,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/40/4b/2028861e724d3bd36227adfa20d3fd24c3fc6d52032f4a93c133be5d17ce/platformdirs-4.4.0-py3-none-any.whl", hash = "sha256:abd01743f24e5287cd7a5db3752faf1a2d65353f38ec26d98e25a6db65958c85", size = 18654, upload-time = "2025-08-26T14:32:02.735Z" },
 ]
 
+[[package]]
+name = "plotext"
+version = "5.3.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c9/d7/f75f397af966fe252d0d34ffd3cae765317fce2134f925f95e7d6725d1ce/plotext-5.3.2.tar.gz", hash = "sha256:52d1e932e67c177bf357a3f0fe6ce14d1a96f7f7d5679d7b455b929df517068e", size = 61967, upload-time = "2024-09-24T15:13:37.728Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f6/1e/12fe7c40cd2099a1f454518754ed229b01beaf3bbb343127f0cc13ce6c22/plotext-5.3.2-py3-none-any.whl", hash = "sha256:394362349c1ddbf319548cfac17ca65e6d5dfc03200c40dfdc0503b3e95a2283", size = 64047, upload-time = "2024-09-24T15:13:36.296Z" },
+]
+
 [[package]]
 name = "pluggy"
 version = "1.6.0"
@@ -4852,6 +4948,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/27/72/0824c18f3bc75810f55dacc2dd933f6ec829771180245ae3cc976195dec0/prometheus_fastapi_instrumentator-7.1.0-py3-none-any.whl", hash = "sha256:978130f3c0bb7b8ebcc90d35516a6fe13e02d2eb358c8f83887cdef7020c31e9", size = 19296, upload-time = "2025-03-19T19:35:04.323Z" },
 ]
 
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.52"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wcwidth" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198, upload-time = "2025-08-27T15:24:02.057Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/84/03/0d3ce49e2505ae70cf43bc5bb3033955d2fc9f932163e84dc0779cc47f48/prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955", size = 391431, upload-time = "2025-08-27T15:23:59.498Z" },
+]
+
 [[package]]
 name = "propcache"
 version = "0.3.2"
@@ -5671,6 +5779,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ef/33/d8df6a2b214ffbe4138db9a1efe3248f67dc3c671f82308bea1582ecbbb7/qdrant_client-1.15.1-py3-none-any.whl", hash = "sha256:2b975099b378382f6ca1cfb43f0d59e541be6e16a5892f282a4b8de7eff5cb63", size = 337331, upload-time = "2025-07-31T19:35:17.539Z" },
 ]
 
+[[package]]
+name = "questionary"
+version = "2.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "prompt-toolkit" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f6/45/eafb0bba0f9988f6a2520f9ca2df2c82ddfa8d67c95d6625452e97b204a5/questionary-2.1.1.tar.gz", hash = "sha256:3d7e980292bb0107abaa79c68dd3eee3c561b83a0f89ae482860b181c8bd412d", size = 25845, upload-time = "2025-08-28T19:00:20.851Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3c/26/1062c7ec1b053db9e499b4d2d5bc231743201b74051c973dadeac80a8f43/questionary-2.1.1-py3-none-any.whl", hash = "sha256:a51af13f345f1cdea62347589fbb6df3b290306ab8930713bfae4d475a7d4a59", size = 36753, upload-time = "2025-08-28T19:00:19.56Z" },
+]
+
 [[package]]
 name = "ray"
 version = "2.49.2"
@@ -5875,15 +5995,16 @@ wheels = [
 
 [[package]]
 name = "rich"
-version = "14.1.0"
+version = "13.9.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "markdown-it-py" },
     { name = "pygments" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/fe/75/af448d8e52bf1d8fa6a9d089ca6c07ff4453d86c65c145d0a300bb073b9b/rich-14.1.0.tar.gz", hash = "sha256:e497a48b844b0320d45007cdebfeaeed8db2a4f4bcf49f15e455cfc4af11eaa8", size = 224441, upload-time = "2025-07-25T07:32:58.125Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ab/3a/0316b28d0761c6734d6bc14e770d85506c986c85ffb239e688eeaab2c2bc/rich-13.9.4.tar.gz", hash = "sha256:439594978a49a09530cff7ebc4b5c7103ef57baf48d5ea3184f21d9a2befa098", size = 223149, upload-time = "2024-11-01T16:43:57.873Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e3/30/3c4d035596d3cf444529e0b2953ad0466f6049528a879d27534700580395/rich-14.1.0-py3-none-any.whl", hash = "sha256:536f5f1785986d6dbdea3c75205c473f970777b4a0d6c6dd1b696aa05a3fa04f", size = 243368, upload-time = "2025-07-25T07:32:56.73Z" },
+    { url = "https://files.pythonhosted.org/packages/19/71/39c7c0d87f8d4e6c020a393182060eaefeeae6c01dab6a84ec346f2567df/rich-13.9.4-py3-none-any.whl", hash = "sha256:6049d5e6ec054bf2779ab3358186963bac2ea89175919d699e378b99738c2a90", size = 242424, upload-time = "2024-11-01T16:43:55.817Z" },
 ]
 
 [[package]]
@@ -6456,6 +6577,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686", size = 9755, upload-time = "2023-10-24T04:13:38.866Z" },
 ]
 
+[[package]]
+name = "sigtools"
+version = "4.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5f/db/669ca14166814da187b3087b908ca924cf83f5b504fe23b3859a3ef67d4f/sigtools-4.0.1.tar.gz", hash = "sha256:4b8e135a9cd4d2ea00da670c093372d74e672ba3abb87f4c98d8e73dea54445c", size = 71910, upload-time = "2022-10-13T07:03:54.149Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1f/91/853dbf6ec096197dba9cd5fd0c836c5fc19142038b7db60ebe6332b1bab1/sigtools-4.0.1-py2.py3-none-any.whl", hash = "sha256:d216b4cf920bbab0fce636ddc429ed8463a5b533d9e1492acb45a2a1bc36ac6c", size = 76419, upload-time = "2022-10-13T07:03:52.658Z" },
+]
+
 [[package]]
 name = "simli-ai"
 version = "0.1.19"
@@ -6925,6 +7058,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" },
 ]
 
+[[package]]
+name = "synchronicity"
+version = "0.7.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sigtools" },
+    { name = "typing-extensions" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/21/b9/29cffab717558ba9bb9a2f2a32279b279f94ce038db060922cd82fcde4a9/synchronicity-0.7.7-py3-none-any.whl", hash = "sha256:916294c8e417395b181dd190a6c7725de2d387d9089db6d8d850349daf3ab9a2", size = 31561, upload-time = "2024-09-26T14:50:45.831Z" },
+]
+
 [[package]]
 name = "tabulate"
 version = "0.9.0"
@@ -6943,6 +7088,35 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d2/3f/8ba87d9e287b9d385a02a7114ddcef61b26f86411e121c9003eb509a1773/tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687", size = 28165, upload-time = "2024-07-05T07:25:29.591Z" },
 ]
 
+[[package]]
+name = "textual"
+version = "6.2.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py", extra = ["linkify", "plugins"] },
+    { name = "platformdirs" },
+    { name = "pygments" },
+    { name = "rich" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a2/30/38b615f7d4b16f6fdd73e4dcd8913e2d880bbb655e68a076e3d91181a7ee/textual-6.2.1.tar.gz", hash = "sha256:4699d8dfae43503b9c417bd2a6fb0da1c89e323fe91c4baa012f9298acaa83e1", size = 1570645, upload-time = "2025-10-01T16:11:24.467Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c5/93/02c7adec57a594af28388d85da9972703a4af94ae1399542555cd9581952/textual-6.2.1-py3-none-any.whl", hash = "sha256:3c7190633cd4d8bfe6049ae66808b98da91ded2edb85cef54e82bf77b03d2a54", size = 710702, upload-time = "2025-10-01T16:11:22.161Z" },
+]
+
+[[package]]
+name = "textual-plotext"
+version = "1.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "plotext" },
+    { name = "textual" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9a/b0/e4e0f38df057db778252db0dd2c08522d7222b8537b6a0181d797b9044bd/textual_plotext-1.0.1.tar.gz", hash = "sha256:836f53a3316756609e194129a35c2875638e7958c261f541e0a794f7c98011be", size = 16489, upload-time = "2024-11-30T19:25:56.625Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/35/53/fba7da208f9d3f59254413660fa0aa6599f2aca806f3ae356670455fd4ea/textual_plotext-1.0.1-py3-none-any.whl", hash = "sha256:6b6bfd00b29f121ddf216eaaf9bdac9d688ed72f40028484d279a10cbbb169ed", size = 16558, upload-time = "2024-11-30T19:25:32.208Z" },
+]
+
 [[package]]
 name = "tiktoken"
 version = "0.11.0"
@@ -7232,7 +7406,7 @@ wheels = [
 
 [[package]]
 name = "typer"
-version = "0.19.2"
+version = "0.16.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
@@ -7240,9 +7414,9 @@ dependencies = [
     { name = "shellingham" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/21/ca/950278884e2ca20547ff3eb109478c6baf6b8cf219318e6bc4f666fad8e8/typer-0.19.2.tar.gz", hash = "sha256:9ad824308ded0ad06cc716434705f691d4ee0bfd0fb081839d2e426860e7fdca", size = 104755, upload-time = "2025-09-23T09:47:48.256Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/43/78/d90f616bf5f88f8710ad067c1f8705bf7618059836ca084e5bb2a0855d75/typer-0.16.1.tar.gz", hash = "sha256:d358c65a464a7a90f338e3bb7ff0c74ac081449e53884b12ba658cbd72990614", size = 102836, upload-time = "2025-08-18T19:18:22.898Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/00/22/35617eee79080a5d071d0f14ad698d325ee6b3bf824fc0467c03b30e7fa8/typer-0.19.2-py3-none-any.whl", hash = "sha256:755e7e19670ffad8283db353267cb81ef252f595aa6834a0d1ca9312d9326cb9", size = 46748, upload-time = "2025-09-23T09:47:46.777Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/76/06dbe78f39b2203d2a47d5facc5df5102d0561e2807396471b5f7c5a30a1/typer-0.16.1-py3-none-any.whl", hash = "sha256:90ee01cb02d9b8395ae21ee3368421faf21fa138cb2a541ed369c08cec5237c9", size = 46397, upload-time = "2025-08-18T19:18:21.663Z" },
 ]
 
 [[package]]
@@ -7288,6 +7462,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611, upload-time = "2025-10-01T02:14:40.154Z" },
 ]
 
+[[package]]
+name = "uc-micro-py"
+version = "1.0.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/91/7a/146a99696aee0609e3712f2b44c6274566bc368dfe8375191278045186b8/uc-micro-py-1.0.3.tar.gz", hash = "sha256:d321b92cff673ec58027c04015fcaa8bb1e005478643ff4a500882eaab88c48a", size = 6043, upload-time = "2024-02-09T16:52:01.654Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/37/87/1f677586e8ac487e29672e4b17455758fce261de06a0d086167bb760361a/uc_micro_py-1.0.3-py3-none-any.whl", hash = "sha256:db1dffff340817673d7b466ec86114a9dc0e9d4d9b5ba229d9d60e5c12600cd5", size = 6229, upload-time = "2024-02-09T16:52:00.371Z" },
+]
+
 [[package]]
 name = "ujson"
 version = "5.11.0"
@@ -7657,6 +7840,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/bd/d3/254cea30f918f489db09d6a8435a7de7047f8cb68584477a515f160541d6/watchfiles-1.1.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:923fec6e5461c42bd7e3fd5ec37492c6f3468be0499bc0707b4bbbc16ac21792", size = 454009, upload-time = "2025-06-15T19:06:52.896Z" },
 ]
 
+[[package]]
+name = "wcwidth"
+version = "0.2.14"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/24/30/6b0809f4510673dc723187aeaf24c7f5459922d01e2f794277a3dfb90345/wcwidth-0.2.14.tar.gz", hash = "sha256:4d478375d31bc5395a3c55c40ccdf3354688364cd61c4f6adacaa9215d0b3605", size = 102293, upload-time = "2025-09-22T16:29:53.023Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/af/b5/123f13c975e9f27ab9c0770f514345bd406d0e8d3b7a0723af9d43f710af/wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1", size = 37286, upload-time = "2025-09-22T16:29:51.641Z" },
+]
+
 [[package]]
 name = "websockets"
 version = "13.1"