Agent session

Overview

The AgentSession is the main orchestrator for your voice AI app. The session is responsible for collecting user input, managing the voice pipeline, invoking the LLM, sending the output back to the user, and emits events for observability and control.

Each session requires at least one Agent to orchestrate. The agent is responsible for defining the core AI logic - instructions, tools, etc - of your app. The framework supports the design of custom workflows to orchestrate handoff and delegation between multiple agents.

The following example shows how to begin a simple single-agent session:

from livekit.agents import AgentSession, Agent, RoomInputOptions, inference
from livekit.plugins import noise_cancellation, silero
from livekit.plugins.turn_detector.multilingual import MultilingualModel

session = AgentSession(
    stt="assemblyai/universal-streaming:en",
    llm="openai/gpt-4.1-mini",
    tts="cartesia/sonic-3:9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",
    vad=silero.VAD.load(),
    turn_detection=MultilingualModel(),
)

await session.start(
    room=ctx.room,
    agent=Agent(instructions="You are a helpful voice AI assistant."),
    room_input_options=RoomInputOptions(
        noise_cancellation=noise_cancellation.BVC(),
    ),
)

import { voice, inference } from '@livekit/agents';
import * as livekit from '@livekit/agents-plugin-livekit';
import * as silero from '@livekit/agents-plugin-silero';
import { BackgroundVoiceCancellation } from '@livekit/noise-cancellation-node';

const vad = await silero.VAD.load();

const session = new voice.AgentSession({
  vad,
  stt: "assemblyai/universal-streaming:en",
  llm: "openai/gpt-4.1-mini",
  tts: "cartesia/sonic-3:9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",
  turnDetection: new livekit.turnDetector.MultilingualModel(),
});

await session.start({
  room: ctx.room,
  agent: new voice.Agent({
    instructions: "You are a helpful voice AI assistant.",
  }),
  inputOptions: {
    noiseCancellation: BackgroundVoiceCancellation(),
  },
});

Lifecycle

An AgentSession progresses through several distinct phases during its operation:

• Initializing: The session is setting up. During initialization, no audio or video processing occurs yet. Agent state is set to initializing.
• Starting: The session is started using the start() method. It sets up I/O connections, initializes agent activity tracking, and begins forwarding audio and video frames. In this phase, the agent is transitioned into the listening state.
• Running: The session is actively processing user input and generating agent responses. During this phase, your agent controls the session and can transfer control to other agents. In this phase, the agent transitions between listening, thinking, and speaking states.
• Closing: When a session is closed, the cleanup process includes gracefully draining pending speech (if requested), waiting for any queued operations to complete, committing any remaining user transcripts, and closing all I/O connections. The session emits a close event and resets internal state.

The following diagram shows the lifecycle of an AgentSession using agent states:

You can monitor agent state changes via the agent_state_changed event.

Events

AgentSession emits events throughout its lifecycle to provide visibility into the conversation flow. For more information, select the event name to see the properties and example code.

Session options

The AgentSession constructor accepts numerous options to configure behavior. The following sections describe the available options grouped by category.

AI models

Configure the default speech and language models for your agent session. You can override these models for specific agents or tasks. To learn more about models, see the models topic.

Turn detection & interruptions

Turn detection and interruptions are critical for managing conversation flow. The session provides several options to configure this behavior. For more information, see Session configuration.

Tools and capabilities

Extend agent capabilities with tools:

• tools: List of FunctionTool or RawFunctionTool objects shared by all agents in the session.

• mcp_servers: List of MCP (Model Context Protocol) servers providing external tools.

• max_tool_steps: Maximum consecutive tool calls per LLM turn. Default: 3.

• ivr_detection: Whether to detect if the agent is interacting with an Interactive Voice Response (IVR) system. Default: False. To learn more, see DTMF.

User interaction

Configure user state and timing:

• user_away_timeout: Time in seconds of silence before setting user state to away. Set to None to turn off. Default: 15.0 seconds.

• min_consecutive_speech_delay: Minimum delay in seconds between consecutive agent utterances. Default: 0.0 seconds.

Text processing

Control how text is processed:

• tts_text_transforms: Transforms to apply to TTS input text. Built-in transforms include "filter_markdown" and "filter_emoji". Set to None to turn off. When not given, all filters are applied by default.

• use_tts_aligned_transcript: Whether to use TTS-aligned transcript as input for the transcription node. Only applies if the TTS supports aligned transcripts. Default: turned off.

Performance optimization

Optimize response latency:

preemptive_generation: Whether to speculatively begin LLM and TTS requests before an end-of-turn is detected. When True, the agent sends inference calls as soon as a user transcript is received. This can reduce response latency but can incur extra compute costs if the user interrupts. Default: False.

Video sampling

Control video frame processing:

video_sampler: Custom video sampler function or None. When not given, uses VoiceActivityVideoSampler which captures at ~1 fps while speaking and ~0.3 fps when silent. To learn more, see Video.

Other options

userdata: Arbitrary per-session user data accessible via session.userdata. To learn more, see Passing state.

rtc_session options

The following optional parameters are available when you define your entrypoint function using the rtc_session decorator:

• agent_name: Name of agent for agent disaptch. If this is set, the agent must be explicitly dispatched to a room. To learn more, see Agent dispatch.
• type: Agent server type determines when a new instance of the agent is created: for each room or for each publisher in a room. To learn more, see Agent server type.
• on_session_end: Callback function to be called when the session ends. To learn more, see Session reports.
• on_request: Callback function to be called when a new request is received. To learn more see Request handler.

RoomIO

Communication between agent and user participants happens using media streams, also known as tracks. For voice AI apps, this is primarily audio, but can include vision. By default, track management is handled by RoomIO, a utility class that serves as a bridge between the agent session and the LiveKit room. When an AgentSession is initiated, it automatically creates a RoomIO object that enables all room participants to subscribe to available audio tracks.

When starting an AgentSession, you can configure how the session interacts with the LiveKit room by passing room_input_options and room_output_options to the start() method. These options control media track management, participant linking, and I/O behavior.

Room input options

Configure how the agent receives input from room participants:

Media configuration

• audio_enabled: Whether to receive audio from the room. Default: True.
• video_enabled: Whether to receive video from the room. Default: False.
• text_enabled: Whether to receive text input from the room. Default: True.
• audio_sample_rate: Audio sample rate in Hz. Default: 24000.
• audio_num_channels: Number of audio channels. Default: 1.
• noise_cancellation: Noise cancellation options to apply to incoming audio.

Connection behavior

• close_on_disconnect: Whether to close the AgentSession if the linked participant disconnects. Default: True.
• delete_room_on_close: Whether to delete the room when the AgentSession closes. Default: True.

Callbacks

text_input_cb: Callback function to handle text input from participants. To learn more, see Custom handling of text input.

Room output options

Configure how the agent publishes output to the room:

Media configuration

• audio_enabled: Whether to publish audio to the room. Default: True.
• transcription_enabled: Whether to publish transcription to the room. Default: True.
• audio_sample_rate: Audio sample rate in Hz. Default: 24000.
• audio_num_channels: Number of audio channels. Default: 1.

Audio publishing

• audio_track_name: The name of the audio track to publish. Default: "roomio_audio".
• audio_publish_options: Track publish options for the audio track (source, encryption, etc.).

Transcription synchronization

• sync_transcription: Whether to synchronize transcription with audio output. When True, transcription is emitted in sync with the audio. When False, transcription is emitted as quickly as available. Default: True.
• transcription_speed_factor: Speed factor for transcription synchronization (only effective if sync_transcription is True). Default: 1.0.

Example usage

from livekit.agents import voice, RoomInputOptions, RoomOutputOptions
from livekit.plugins import noise_cancellation

input_options = RoomInputOptions(
    video_enabled=True,
    noise_cancellation=noise_cancellation.BVC(),
    participant_identity="user_123",
)

output_options = RoomOutputOptions(
    sync_transcription=False,
    audio_track_name="agent_voice",
)

await session.start(
    agent=my_agent,
    room=room,
    room_input_options=input_options,
    room_output_options=output_options,
)

To learn more about publishing audio and video, see the following topics:

Custom RoomIO

For greater control over media sharing in a room, you can create a custom RoomIO object. For example, you might want to manually control which input and output devices are used, or to control which participants an agent listens to or responds to.

To replace the default one created in AgentSession, create a RoomIO object in your entrypoint function and pass it an instance of the AgentSession in the constructor. For examples, see the following in the repository: