# # Copyright (c) 2024-2026, Daily # # SPDX-License-Identifier: BSD 2-Clause License # """ Grok Voice Agent Realtime Example This example demonstrates using xAI's Grok Voice Agent API for real-time voice conversations. The Grok Voice Agent provides: - Real-time audio streaming with low latency - Built-in voice activity detection (VAD) - Multiple voice options (Ara, Rex, Sal, Eve, Leo) - Built-in tools: web_search, x_search, file_search - Custom function calling Requirements: - XAI_API_KEY environment variable set - pip install pipecat-ai[grok] Usage: python 50-grok-realtime.py --transport webrtc python 50-grok-realtime.py --transport daily """ import os from datetime import datetime 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 # Note: Grok has built-in server-side VAD, so we don't need local VAD # from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.frames.frames import LLMRunFrame from pipecat.observers.loggers.transcription_log_observer import ( TranscriptionLogObserver, ) 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 ( AssistantTurnStoppedMessage, LLMContextAggregatorPair, UserTurnStoppedMessage, ) from pipecat.runner.types import RunnerArguments from pipecat.runner.utils import create_transport from pipecat.services.llm_service import FunctionCallParams from pipecat.services.xai.realtime.events import SessionProperties from pipecat.services.xai.realtime.llm import GrokRealtimeLLMService 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) # --- Function Handlers --- async def fetch_weather_from_api(params: FunctionCallParams): """Handle weather function calls.""" temperature = 75 if params.arguments.get("format") == "fahrenheit" else 24 await params.result_callback( { "conditions": "nice", "temperature": temperature, "format": params.arguments.get("format", "celsius"), "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"), } ) async def get_current_time(params: FunctionCallParams): """Handle time function calls.""" await params.result_callback( { "time": datetime.now().strftime("%H:%M:%S"), "date": datetime.now().strftime("%Y-%m-%d"), "timezone": "local", } ) async def get_restaurant_recommendation(params: FunctionCallParams): """Handle restaurant recommendation function calls.""" location = params.arguments.get("location", "unknown") await params.result_callback( { "name": "The Golden Dragon", "cuisine": "Chinese", "location": location, "rating": 4.5, } ) # --- Function Schemas --- weather_function = FunctionSchema( name="get_current_weather", description="Get the current weather for a location", 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.", }, }, required=["location", "format"], ) time_function = FunctionSchema( name="get_current_time", description="Get the current time and date", properties={}, required=[], ) restaurant_function = FunctionSchema( name="get_restaurant_recommendation", description="Get a restaurant recommendation for a location", properties={ "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, }, required=["location"], ) # Create tools schema with custom functions tools = ToolsSchema(standard_tools=[weather_function, time_function, restaurant_function]) # --- Transport Configuration --- # Note: We don't need local VAD since Grok has built-in server-side VAD. # Audio sample rates are configured via PipelineParams, not transport params. 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("Starting Grok Voice Agent bot") # Configure Grok session properties session_properties = SessionProperties( # Voice options: Ara, Rex, Sal, Eve, Leo voice="Ara", # Grok-specific built-in tools can be added here: # tools=[ # WebSearchTool(), # Enable web search # XSearchTool(), # Enable X/Twitter search # ], ) # Create the Grok Realtime LLM service llm = GrokRealtimeLLMService( api_key=os.getenv("XAI_API_KEY"), settings=GrokRealtimeLLMService.Settings( system_instruction="""You are a helpful and friendly AI assistant powered by Grok. You have access to several tools: - Weather information - Current time - Restaurant recommendations - Web search (built-in) - X/Twitter search (built-in) Your voice and personality should be warm and engaging. Keep your responses concise and conversational since this is a voice interaction. If the user asks about current events or news, use web search. If they ask about what people are saying on social media, use X search. Always be helpful and proactive in offering assistance.""", session_properties=session_properties, ), ) # Register function handlers llm.register_function("get_current_weather", fetch_weather_from_api) llm.register_function("get_current_time", get_current_time) llm.register_function("get_restaurant_recommendation", get_restaurant_recommendation) # Create context with initial message and tools context = LLMContext( [{"role": "developer", "content": "Say hello and introduce yourself!"}], tools, ) user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context) # Build the pipeline # Note: In realtime mode, transcription comes from Grok (upstream), # so transcript.user() goes BEFORE llm pipeline = Pipeline( [ transport.input(), # Transport user input (audio) user_aggregator, llm, # Grok Realtime LLM (handles STT + LLM + TTS) transport.output(), # Transport bot output (audio) assistant_aggregator, ] ) task = PipelineTask( pipeline, params=PipelineParams( enable_metrics=True, enable_usage_metrics=True, ), idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, observers=[TranscriptionLogObserver()], ) @transport.event_handler("on_client_connected") async def on_client_connected(transport, client): logger.info("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("Client disconnected") await task.cancel() # Log transcript updates @user_aggregator.event_handler("on_user_turn_stopped") async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage): timestamp = f"[{message.timestamp}] " if message.timestamp else "" line = f"{timestamp}user: {message.content}" logger.info(f"Transcript: {line}") @assistant_aggregator.event_handler("on_assistant_turn_stopped") async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage): timestamp = f"[{message.timestamp}] " if message.timestamp else "" line = f"{timestamp}assistant: {message.content}" logger.info(f"Transcript: {line}") 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()