tool_calling

title	category	tags	difficulty	description	demonstrates
Tool Calling	basics	tool-calling deepgram openai cartesia	beginner	Shows how to use tool calling in an agent.	Using the most basic form of tool calling in an agent to print to the console

This example shows how to use tool calling in an agent. The agent has a simple function tool that prints a message to the console when invoked. Ask the agent to "print to the console" to see it in action.

Prerequisites

• Add a .env 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:

  pip install "livekit-agents[silero]" python-dotenv

Load environment and define the AgentServer

Import the necessary modules, load environment variables, and create an AgentServer.

import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, AgentServer, cli, Agent, AgentSession, inference, RunContext, function_tool
from livekit.plugins import silero

logger = logging.getLogger("tool-calling")
logger.setLevel(logging.INFO)

load_dotenv()

server = AgentServer()

Prewarm VAD for faster connections

Preload the VAD model once per process to reduce connection latency.

def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()

server.setup_fnc = prewarm

Define the agent with a function tool

Create a lightweight Agent that only contains instructions and a function tool. The @function_tool decorator exposes the method as a callable tool to the LLM. The method returns a tuple of (result, message) where the message is what the agent says after calling the tool.

class ToolCallingAgent(Agent):
    def __init__(self) -> None:
        super().__init__(
            instructions="""
                You are a helpful assistant communicating through voice. Don't use any unpronouncable characters.
                Note: If asked to print to the console, use the `print_to_console` function.
            """
        )

    @function_tool
    async def print_to_console(self, context: RunContext):
        print("Console Print Success!")
        return None, "I've printed to the console."

    async def on_enter(self):
        self.session.generate_reply()

Create the RTC session entrypoint

Create an AgentSession with STT/LLM/TTS/VAD configured, start the session with the agent, and connect to the room.

@server.rtc_session()
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-4.1-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=ToolCallingAgent(), room=ctx.room)
    await ctx.connect()

Run the server

The cli.run_app() function starts the agent server and manages the worker lifecycle.

if __name__ == "__main__":
    cli.run_app(server)

Run it

python tool_calling.py console

How it works

1. The agent starts and generates an initial greeting via on_enter.
2. When the user asks to print to the console, the LLM calls the print_to_console function.
3. The function executes and prints "Console Print Success!" to the terminal.
4. The agent speaks "I've printed to the console" as confirmation.

Full example

import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, AgentServer, cli, Agent, AgentSession, inference, RunContext, function_tool
from livekit.plugins import silero

logger = logging.getLogger("tool-calling")
logger.setLevel(logging.INFO)

load_dotenv()

class ToolCallingAgent(Agent):
    def __init__(self) -> None:
        super().__init__(
            instructions="""
                You are a helpful assistant communicating through voice. Don't use any unpronouncable characters.
                Note: If asked to print to the console, use the `print_to_console` function.
            """
        )

    @function_tool
    async def print_to_console(self, context: RunContext):
        print("Console Print Success!")
        return None, "I've printed to the console."

    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()
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-4.1-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=ToolCallingAgent(), room=ctx.room)
    await ctx.connect()

if __name__ == "__main__":
    cli.run_app(server)