LiveKit docs › Audio › Playing Audio --- # Playing Audio > Shows how to play audio from a file in an agent. This example shows how to expose a function tool that plays a local WAV file into the call. The agent reads the file, wraps it in an audio frame, and streams it via `session.say`. ## Prerequisites - Add a `.env.local` in this directory with your LiveKit credentials:``` LIVEKIT_URL=your_livekit_url LIVEKIT_API_KEY=your_api_key LIVEKIT_API_SECRET=your_api_secret ``` - Install dependencies:```bash pip install "livekit-agents[silero]" python-dotenv ``` - Place an `audio.wav` file in the same directory as the script ## Load environment, logging, and define an AgentServer Load environment variables, configure logging, and initialize the AgentServer. ```python import logging from pathlib import Path import wave from dotenv import load_dotenv from livekit.agents import JobContext, JobProcess, AgentServer, cli, Agent, AgentSession, inference, RunContext, function_tool from livekit.plugins import silero from livekit import rtc load_dotenv(".env.local") logger = logging.getLogger("playing-audio") logger.setLevel(logging.INFO) server = AgentServer() ``` ## Define the agent with audio playback tool Create a lightweight agent with instructions and a function tool that reads a WAV file, builds an `AudioFrame`, and streams it to the user. ```python class AudioPlayerAgent(Agent): def __init__(self) -> None: super().__init__( instructions=""" You are a helpful assistant communicating through voice. Don't use any unpronouncable characters. If asked to play audio, use the `play_audio_file` function. """ ) @function_tool async def play_audio_file(self, context: RunContext): """Play a local audio file""" audio_path = Path(__file__).parent / "audio.wav" with wave.open(str(audio_path), 'rb') as wav_file: num_channels = wav_file.getnchannels() sample_rate = wav_file.getframerate() frames = wav_file.readframes(wav_file.getnframes()) audio_frame = rtc.AudioFrame( data=frames, sample_rate=sample_rate, num_channels=num_channels, samples_per_channel=wav_file.getnframes() ) async def audio_generator(): yield audio_frame await self.session.say("Playing audio file", audio=audio_generator()) return None, "I've played the audio file for you." async def on_enter(self): self.session.generate_reply() ``` ## Prewarm VAD for faster connections Preload the VAD model once per process to reduce connection latency. ```python def prewarm(proc: JobProcess): proc.userdata["vad"] = silero.VAD.load() server.setup_fnc = prewarm ``` ## Define the rtc session entrypoint Create the session with STT/LLM/TTS configuration and start the audio player agent. ```python @server.rtc_session(agent_name="my-agent") async def entrypoint(ctx: JobContext): ctx.log_context_fields = {"room": ctx.room.name} session = AgentSession( stt=inference.STT(model="deepgram/nova-3-general"), llm=inference.LLM(model="openai/gpt-5-mini"), tts=inference.TTS(model="cartesia/sonic-3", voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc"), vad=ctx.proc.userdata["vad"], preemptive_generation=True, ) await session.start(agent=AudioPlayerAgent(), room=ctx.room) await ctx.connect() ``` ## Run the server Start the agent server with the CLI runner. ```python if __name__ == "__main__": cli.run_app(server) ``` ## Run it ```bash python playing_audio.py console ``` ## How it works 1. The agent greets the user on entry. 2. The LLM can invoke `play_audio_file` when asked to play audio. 3. The tool reads a local WAV file, wraps it in an `AudioFrame`, and streams it via `session.say`. 4. A short spoken preamble ("Playing audio file") plays before the audio clip. 5. The rest of the media pipeline continues unchanged. ## Full example ```python import logging from pathlib import Path import wave from dotenv import load_dotenv from livekit.agents import JobContext, JobProcess, AgentServer, cli, Agent, AgentSession, inference, RunContext, function_tool from livekit.plugins import silero from livekit import rtc load_dotenv(".env.local") logger = logging.getLogger("playing-audio") logger.setLevel(logging.INFO) class AudioPlayerAgent(Agent): def __init__(self) -> None: super().__init__( instructions=""" You are a helpful assistant communicating through voice. Don't use any unpronouncable characters. If asked to play audio, use the `play_audio_file` function. """ ) @function_tool async def play_audio_file(self, context: RunContext): """Play a local audio file""" audio_path = Path(__file__).parent / "audio.wav" with wave.open(str(audio_path), 'rb') as wav_file: num_channels = wav_file.getnchannels() sample_rate = wav_file.getframerate() frames = wav_file.readframes(wav_file.getnframes()) audio_frame = rtc.AudioFrame( data=frames, sample_rate=sample_rate, num_channels=num_channels, samples_per_channel=wav_file.getnframes() ) async def audio_generator(): yield audio_frame await self.session.say("Playing audio file", audio=audio_generator()) return None, "I've played the audio file for you." async def on_enter(self): self.session.generate_reply() server = AgentServer() def prewarm(proc: JobProcess): proc.userdata["vad"] = silero.VAD.load() server.setup_fnc = prewarm @server.rtc_session(agent_name="my-agent") async def entrypoint(ctx: JobContext): ctx.log_context_fields = {"room": ctx.room.name} session = AgentSession( stt=inference.STT(model="deepgram/nova-3-general"), llm=inference.LLM(model="openai/gpt-5-mini"), tts=inference.TTS(model="cartesia/sonic-3", voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc"), vad=ctx.proc.userdata["vad"], preemptive_generation=True, ) await session.start(agent=AudioPlayerAgent(), room=ctx.room) await ctx.connect() if __name__ == "__main__": cli.run_app(server) ``` --- This document was rendered at 2026-06-07T11:33:39.774Z. For the latest version of this document, see [https://docs.livekit.io/reference/recipes/playing_audio.md](https://docs.livekit.io/reference/recipes/playing_audio.md). To explore all LiveKit documentation, see [llms.txt](https://docs.livekit.io/llms.txt).