Reorganize examples into topic-based subfolders
Move 304 examples from a flat numbered directory into 14 descriptive subfolders: getting-started, services (speech + function-calling), transcription, vision, realtime, persistent-context, context-summarization, update-settings (stt/tts/llm), turn-management, thinking-and-mcp, transports, video-avatar, video-processing, and features. Strip numbered prefixes from filenames (e.g. 07c-interruptible-deepgram.py becomes services/speech/deepgram.py) since the folder context makes them redundant. Keep numbered prefixes only in getting-started/ where ordering matters. Update eval script paths and README to match the new structure.
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Mark Backman
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examples/realtime/grok.py
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278
examples/realtime/grok.py
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""
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Grok Voice Agent Realtime Example
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This example demonstrates using xAI's Grok Voice Agent API for real-time
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voice conversations. The Grok Voice Agent provides:
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- Real-time audio streaming with low latency
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- Built-in voice activity detection (VAD)
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- Multiple voice options (Ara, Rex, Sal, Eve, Leo)
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- Built-in tools: web_search, x_search, file_search
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- Custom function calling
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Requirements:
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- XAI_API_KEY environment variable set
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- pip install pipecat-ai[grok]
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Usage:
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python 50-grok-realtime.py --transport webrtc
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python 50-grok-realtime.py --transport daily
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"""
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import os
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from datetime import datetime
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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# Note: Grok has built-in server-side VAD, so we don't need local VAD
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# from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame
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from pipecat.observers.loggers.transcription_log_observer import (
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TranscriptionLogObserver,
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)
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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AssistantTurnStoppedMessage,
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LLMContextAggregatorPair,
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UserTurnStoppedMessage,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.services.xai.realtime.events import SessionProperties
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from pipecat.services.xai.realtime.llm import GrokRealtimeLLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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# --- Function Handlers ---
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async def fetch_weather_from_api(params: FunctionCallParams):
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"""Handle weather function calls."""
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temperature = 75 if params.arguments.get("format") == "fahrenheit" else 24
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await params.result_callback(
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{
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"conditions": "nice",
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"temperature": temperature,
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"format": params.arguments.get("format", "celsius"),
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"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
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}
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)
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async def get_current_time(params: FunctionCallParams):
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"""Handle time function calls."""
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await params.result_callback(
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{
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"time": datetime.now().strftime("%H:%M:%S"),
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"date": datetime.now().strftime("%Y-%m-%d"),
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"timezone": "local",
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}
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)
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async def get_restaurant_recommendation(params: FunctionCallParams):
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"""Handle restaurant recommendation function calls."""
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location = params.arguments.get("location", "unknown")
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await params.result_callback(
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{
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"name": "The Golden Dragon",
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"cuisine": "Chinese",
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"location": location,
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"rating": 4.5,
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}
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)
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# --- Function Schemas ---
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather for a location",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use.",
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},
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},
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required=["location", "format"],
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)
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time_function = FunctionSchema(
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name="get_current_time",
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description="Get the current time and date",
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properties={},
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required=[],
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)
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restaurant_function = FunctionSchema(
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name="get_restaurant_recommendation",
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description="Get a restaurant recommendation for a location",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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},
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required=["location"],
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)
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# Create tools schema with custom functions
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tools = ToolsSchema(standard_tools=[weather_function, time_function, restaurant_function])
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# --- Transport Configuration ---
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# Note: We don't need local VAD since Grok has built-in server-side VAD.
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# Audio sample rates are configured via PipelineParams, not transport params.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info("Starting Grok Voice Agent bot")
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# Configure Grok session properties
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session_properties = SessionProperties(
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# Voice options: Ara, Rex, Sal, Eve, Leo
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voice="Ara",
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# Grok-specific built-in tools can be added here:
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# tools=[
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# WebSearchTool(), # Enable web search
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# XSearchTool(), # Enable X/Twitter search
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# ],
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)
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# Create the Grok Realtime LLM service
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llm = GrokRealtimeLLMService(
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api_key=os.getenv("XAI_API_KEY"),
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settings=GrokRealtimeLLMService.Settings(
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system_instruction="""You are a helpful and friendly AI assistant powered by Grok.
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You have access to several tools:
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- Weather information
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- Current time
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- Restaurant recommendations
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- Web search (built-in)
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- X/Twitter search (built-in)
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Your voice and personality should be warm and engaging. Keep your responses
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concise and conversational since this is a voice interaction.
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If the user asks about current events or news, use web search.
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If they ask about what people are saying on social media, use X search.
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Always be helpful and proactive in offering assistance.""",
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session_properties=session_properties,
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),
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)
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# Register function handlers
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llm.register_function("get_current_weather", fetch_weather_from_api)
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llm.register_function("get_current_time", get_current_time)
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llm.register_function("get_restaurant_recommendation", get_restaurant_recommendation)
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# Create context with initial message and tools
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context = LLMContext(
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[{"role": "developer", "content": "Say hello and introduce yourself!"}],
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tools,
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)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(context)
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# Build the pipeline
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# Note: In realtime mode, transcription comes from Grok (upstream),
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# so transcript.user() goes BEFORE llm
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input (audio)
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user_aggregator,
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llm, # Grok Realtime LLM (handles STT + LLM + TTS)
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transport.output(), # Transport bot output (audio)
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assistant_aggregator,
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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observers=[TranscriptionLogObserver()],
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info("Client connected")
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# Kick off the conversation
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info("Client disconnected")
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await task.cancel()
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# Log transcript updates
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@user_aggregator.event_handler("on_user_turn_stopped")
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async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
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timestamp = f"[{message.timestamp}] " if message.timestamp else ""
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line = f"{timestamp}user: {message.content}"
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logger.info(f"Transcript: {line}")
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@assistant_aggregator.event_handler("on_assistant_turn_stopped")
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async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
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timestamp = f"[{message.timestamp}] " if message.timestamp else ""
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line = f"{timestamp}assistant: {message.content}"
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logger.info(f"Transcript: {line}")
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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