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Merge pull request #4191 from pipecat-ai/mb/remove-openpipe

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Remove OpenPipe integration
This commit is contained in:
Mark Backman
2026-03-30 08:52:14 -04:00
committed by GitHub
parent 3eb7c2bcd9 2177e28ee1
commit dd3ca6fbba
11 changed files with 3 additions and 624 deletions
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1
changelog/4191.removed.md Normal file
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@@ -0,0 +1 @@
- ⚠️ Removed `OpenPipeLLMService` and the `openpipe` extra. OpenPipe was acquired by CoreWeave and the package is no longer maintained. If you were using `openpipe` as an LLM provider, switch to the underlying provider directly (e.g. `openai`). The OpenPipe interface can still be used with `OpenAILLMService` by specifying a `base_url`.

1
docs/api/conf.py
View File

@@ -98,7 +98,6 @@ autodoc_mock_imports = [
"cartesia",
"camb",
"sarvamai",
"openpipe",
"openai.types.beta.realtime",
"langchain_core",
"langchain_core.messages",

3
env.example
View File

@@ -136,9 +136,6 @@ NVIDIA_API_KEY=...
# OpenAI
OPENAI_API_KEY=...
# OpenPipe
OPENPIPE_API_KEY=...
# OpenRouter
OPENROUTER_API_KEY=...

130
examples/foundational/07h-interruptible-openpipe.py
View File

@@ -1,130 +0,0 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
import time
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.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openpipe.llm import OpenPipeLLMService
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")
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
),
)
timestamp = int(time.time())
llm = OpenPipeLLMService(
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.",
),
)
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()

176
examples/foundational/14x-function-calling-openpipe.py
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@@ -1,176 +0,0 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
import time
from dotenv import load_dotenv
from loguru import logger
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
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
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.llm_service import FunctionCallParams
from pipecat.services.openpipe.llm import OpenPipeLLMService
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)
async def fetch_weather_from_api(params: FunctionCallParams):
await params.result_callback({"conditions": "nice", "temperature": "75"})
async def fetch_restaurant_recommendation(params: FunctionCallParams):
await params.result_callback({"name": "The Golden Dragon"})
# 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")
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
),
)
timestamp = int(time.time())
llm = OpenPipeLLMService(
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.",
),
)
# You can also register a function_name of None to get all functions
# sent to the same callback with an additional function_name parameter.
llm.register_function("get_current_weather", fetch_weather_from_api)
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
@llm.event_handler("on_function_calls_started")
async def on_function_calls_started(service, function_calls):
await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
weather_function = FunctionSchema(
name="get_current_weather",
description="Get the current weather",
properties={
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the user's location.",
},
},
required=["location", "format"],
)
restaurant_function = FunctionSchema(
name="get_restaurant_recommendation",
description="Get a restaurant recommendation",
properties={
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
},
required=["location"],
)
tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
context = LLMContext(tools=tools)
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,
),
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.
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()

133
examples/foundational/55zzo-update-settings-openpipe-llm.py
View File

@@ -1,133 +0,0 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import time
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame, LLMUpdateSettingsFrame
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.openpipe.llm import OpenPipeLLMService
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 = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
settings=CartesiaTTSService.Settings(
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
),
)
timestamp = int(time.time())
llm = OpenPipeLLMService(
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.",
),
)
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,
),
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")
context.add_message(
{"role": "developer", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])
await asyncio.sleep(10)
logger.info("Updating OpenPipe LLM settings: temperature=0.1")
await task.queue_frame(
LLMUpdateSettingsFrame(delta=OpenPipeLLMService.Settings(temperature=0.1))
)
@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()

1
pyproject.toml
View File

@@ -99,7 +99,6 @@ novita = []
nvidia = [ "nvidia-riva-client>=2.25.1,<3" ]
openai = [ "pipecat-ai[websockets-base]" ]
rnnoise = [ "pyrnnoise~=0.4.1" ]
openpipe = [ "openpipe>=4.50.0,<6" ]
openrouter = []
perplexity = []
piper = [ "piper-tts>=1.3.0,<2", "requests>=2.32.5,<3" ]

2
scripts/evals/run-release-evals.py
View File

@@ -113,7 +113,6 @@ TESTS_07 = [
("07f-interruptible-azure-http.py", EVAL_SIMPLE_MATH),
("07g-interruptible-openai.py", EVAL_SIMPLE_MATH),
("07g-interruptible-openai-http.py", EVAL_SIMPLE_MATH),
("07h-interruptible-openpipe.py", EVAL_SIMPLE_MATH),
("07j-interruptible-gladia.py", EVAL_SIMPLE_MATH),
("07j-interruptible-gladia-vad.py", EVAL_SIMPLE_MATH),
("07k-interruptible-lmnt.py", EVAL_SIMPLE_MATH),
@@ -191,7 +190,6 @@ TESTS_14 = [
("14r-function-calling-aws.py", EVAL_WEATHER_AND_RESTAURANT),
("14v-function-calling-nebius.py", EVAL_WEATHER),
("14w-function-calling-mistral.py", EVAL_WEATHER),
("14x-function-calling-openpipe.py", EVAL_WEATHER),
("14y-function-calling-sarvam.py", EVAL_WEATHER),
("14z-function-calling-novita.py", EVAL_WEATHER),
# Video

13
src/pipecat/services/openpipe/__init__.py
View File

@@ -1,13 +0,0 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import sys
from pipecat.services import DeprecatedModuleProxy
from .llm import *
sys.modules[__name__] = DeprecatedModuleProxy(globals(), "openpipe", "openpipe.llm")

143
src/pipecat/services/openpipe/llm.py
View File

@@ -1,143 +0,0 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""OpenPipe LLM service implementation for Pipecat.
This module provides an OpenPipe-specific implementation of the OpenAI LLM service,
enabling integration with OpenPipe's fine-tuning and monitoring capabilities.
"""
from dataclasses import dataclass
from typing import Dict, Optional
from loguru import logger
from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
from pipecat.services.openai.base_llm import BaseOpenAILLMService
from pipecat.services.openai.llm import OpenAILLMService
try:
from openpipe import AsyncOpenAI as OpenPipeAI
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error("In order to use OpenPipe, you need to `pip install pipecat-ai[openpipe]`.")
raise Exception(f"Missing module: {e}")
@dataclass
class OpenPipeLLMSettings(BaseOpenAILLMService.Settings):
"""Settings for OpenPipeLLMService."""
pass
class OpenPipeLLMService(OpenAILLMService):
"""OpenPipe-powered Large Language Model service.
Extends OpenAI's LLM service to integrate with OpenPipe's fine-tuning and
monitoring platform. Provides enhanced request logging and tagging capabilities
for model training and evaluation.
"""
Settings = OpenPipeLLMSettings
_settings: Settings
def __init__(
self,
*,
model: Optional[str] = None,
api_key: Optional[str] = None,
base_url: Optional[str] = None,
openpipe_api_key: Optional[str] = None,
openpipe_base_url: str = "https://app.openpipe.ai/api/v1",
tags: Optional[Dict[str, str]] = None,
settings: Optional[Settings] = None,
**kwargs,
):
"""Initialize OpenPipe LLM service.
Args:
model: The model name to use. Defaults to "gpt-4.1".
.. deprecated:: 0.0.105
Use ``settings=OpenPipeLLMService.Settings(model=...)`` instead.
api_key: OpenAI API key for authentication. If None, reads from environment.
base_url: Custom OpenAI API endpoint URL. Uses default if None.
openpipe_api_key: OpenPipe API key for enhanced features. If None, reads from environment.
openpipe_base_url: OpenPipe API endpoint URL. Defaults to "https://app.openpipe.ai/api/v1".
tags: Optional dictionary of tags to apply to all requests for tracking.
settings: Runtime-updatable settings. When provided alongside deprecated
parameters, ``settings`` values take precedence.
**kwargs: Additional arguments passed to parent OpenAILLMService.
"""
# 1. Initialize default_settings with hardcoded defaults
default_settings = self.Settings(model="gpt-4.1")
# 2. Apply direct init arg overrides (deprecated)
if model is not None:
self._warn_init_param_moved_to_settings("model", "model")
default_settings.model = model
# 3. (No step 3, as there's no params object to apply)
# 4. Apply settings delta (canonical API, always wins)
if settings is not None:
default_settings.apply_update(settings)
super().__init__(
api_key=api_key,
base_url=base_url,
openpipe_api_key=openpipe_api_key,
openpipe_base_url=openpipe_base_url,
settings=default_settings,
**kwargs,
)
self._tags = tags
def create_client(self, api_key=None, base_url=None, **kwargs):
"""Create an OpenPipe client instance.
Args:
api_key: OpenAI API key for authentication.
base_url: OpenAI API base URL.
**kwargs: Additional arguments including openpipe_api_key and openpipe_base_url.
Returns:
Configured OpenPipe AsyncOpenAI client instance.
"""
openpipe_api_key = kwargs.get("openpipe_api_key") or ""
openpipe_base_url = kwargs.get("openpipe_base_url") or ""
client = OpenPipeAI(
api_key=api_key,
base_url=base_url,
openpipe={"api_key": openpipe_api_key, "base_url": openpipe_base_url},
)
return client
def build_chat_completion_params(self, params_from_context: OpenAILLMInvocationParams) -> dict:
"""Build parameters for OpenPipe chat completion request.
Adds OpenPipe-specific logging and tagging parameters.
Args:
params_from_context: Parameters, derived from the LLM context, to
use for the chat completion. Contains messages, tools, and tool
choice.
Returns:
Dictionary of parameters for the chat completion request.
"""
# Start with base parameters
params = super().build_chat_completion_params(params_from_context)
# Add OpenPipe-specific parameters
params["openpipe"] = {
"tags": self._tags,
"log_request": True,
}
return params

24
uv.lock generated
View File

@@ -1,5 +1,5 @@
version = 1
revision = 2
revision = 3
requires-python = ">=3.10"
resolution-markers = [
"python_full_version >= '3.14'",
@@ -4251,22 +4251,6 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/e9/a5/1be1516390333ff9be3a9cb648c9f33df79d5096e5884b5df71a588af463/opencv_python-4.13.0.92-cp37-abi3-win_amd64.whl", hash = "sha256:423d934c9fafb91aad38edf26efb46da91ffbc05f3f59c4b0c72e699720706f5", size = 40212062, upload-time = "2026-02-05T07:02:12.724Z" },
]
[[package]]
name = "openpipe"
version = "5.0.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "anthropic" },
{ name = "attrs" },
{ name = "httpx" },
{ name = "openai" },
{ name = "python-dateutil" },
]
sdist = { url = "https://files.pythonhosted.org/packages/7c/34/b487bc0ff60d3ed634e6f7bc34b5138f04e6ae319cc6578001822df93901/openpipe-5.0.0.tar.gz", hash = "sha256:040acc526fece42ba505fcedd8cd584f42482c9bd01f16b2538c9ea9c82882f4", size = 98910, upload-time = "2025-07-31T01:36:29.482Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/7a/5e/516010c25a32884a87e1f8303a292f3981fa382cc7570a9ed88fb28681d5/openpipe-5.0.0-py3-none-any.whl", hash = "sha256:c04af7afb4d9bcd52e1250757dd93d0e0ed19c9ff4b524f131dd94aadf4c1a9b", size = 439951, upload-time = "2025-07-31T01:36:28.003Z" },
]
[[package]]
name = "opentelemetry-api"
version = "1.40.0"
@@ -4781,9 +4765,6 @@ nvidia = [
openai = [
{ name = "websockets" },
]
openpipe = [
{ name = "openpipe" },
]
piper = [
{ name = "piper-tts" },
{ name = "requests" },
@@ -4933,7 +4914,6 @@ requires-dist = [
{ name = "onnxruntime", specifier = "~=1.23.2" },
{ name = "openai", specifier = ">=1.74.0,<3" },
{ name = "opencv-python", marker = "extra == 'webrtc'", specifier = ">=4.11.0.86,<5" },
{ name = "openpipe", marker = "extra == 'openpipe'", specifier = ">=4.50.0,<6" },
{ name = "opentelemetry-api", marker = "extra == 'tracing'", specifier = ">=1.33.0,<2" },
{ name = "opentelemetry-instrumentation", marker = "extra == 'tracing'", specifier = ">=0.54b0,<1" },
{ name = "opentelemetry-sdk", marker = "extra == 'tracing'", specifier = ">=1.33.0,<2" },
@@ -4996,7 +4976,7 @@ requires-dist = [
{ name = "wait-for2", marker = "python_full_version < '3.12'", specifier = ">=0.4.1,<1" },
{ name = "websockets", marker = "extra == 'websockets-base'", specifier = ">=13.1,<16.0" },
]
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provides-extras = ["aic", "anthropic", "assemblyai", "asyncai", "aws", "aws-nova-sonic", "azure", "cartesia", "camb", "cerebras", "daily", "deepgram", "deepseek", "elevenlabs", "fal", "fireworks", "fish", "gladia", "google", "gradium", "grok", "groq", "gstreamer", "heygen", "hume", "inworld", "koala", "kokoro", "krisp", "langchain", "lemonslice", "livekit", "lmnt", "local", "local-smart-turn", "mcp", "mem0", "mistral", "mlx-whisper", "moondream", "nebius", "neuphonic", "noisereduce", "novita", "nvidia", "openai", "rnnoise", "openrouter", "perplexity", "piper", "qwen", "remote-smart-turn", "resembleai", "rime", "riva", "runner", "sagemaker", "sambanova", "sarvam", "sentry", "silero", "simli", "smallest", "soniox", "soundfile", "speechmatics", "strands", "tavus", "together", "tracing", "ultravox", "webrtc", "websocket", "websockets-base", "whisper", "xai"]
[package.metadata.requires-dev]
dev = [