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Agent Session

The AgentSession is the central orchestrator that integrates the Agent, Pipeline, and optional ConversationFlow into a cohesive workflow. It manages the complete lifecycle of an agent's interaction within a VideoSDK meeting, handling initialization, execution, and cleanup.

Agent Session

Core Features​

  • Component Orchestration: Unifies agent, pipeline, and conversation flow components.
  • Lifecycle Management: Handles session start, execution, and cleanup

State Management​

The AgentSession provides comprehensive state tracking for both users and agents, automatically emitting state change events for real-time monitoring.

Version Requirement

The state management features and enhanced methods (reply(), interrupt()) are available in versions above v0.0.35.

User States​

  • IDLE - User is not actively speaking or listening
  • SPEAKING - User is currently speaking
  • LISTENING - User is actively listening to the agent

Agent States​

  • STARTING - Agent is initializing
  • IDLE - Agent is ready and waiting
  • SPEAKING - Agent is currently generating speech
  • LISTENING - Agent is processing user input
  • THINKING - Agent is processing and generating response
  • CLOSING - Agent is shutting down

State Event Monitoring​

State changes are automatically emitted as events that you can listen to:

main.py
def on_user_state_changed(data):  
    print("User state:", data)  
  
def on_agent_state_changed(data):  
    print("Agent state:", data)  
  
session.on("user_state_changed", on_user_state_changed)  
session.on("agent_state_changed", on_agent_state_changed)

Constructor Parameters​

AgentSession(
    agent: Agent,
    pipeline: Pipeline,
    conversation_flow: Optional[ConversationFlow] = None,
    wake_up: Optional[int] = None
)

Agent

Your custom agent implementation

Pipeline

Either RealTimePipeline or CascadingPipeline

Conversation Flow

Optional conversation state management

Wake-Up Call​

Wake-up call automatically triggers actions when users are inactive for a specified period of time, helping maintain engagement.

main.py
# Configure wake-up timer  
session = AgentSession(  
    agent=MyAgent(),  
    pipeline=pipeline,  
    wake_up=10  # Trigger after 10 seconds of inactivity  
)  
  
# Set callback function  
async def on_wake_up():  
    await session.say("Are you still there? How can I help?")  
  
session.on_wake_up = on_wake_up
note

Important: If a wake_up time is provided, you must set a callback function before starting the session. If no wake_up time is specified, no timer or callback will be activated.

Basic Usage​

To get an agent running, you initialize an AgentSession with your custom Agent and a configured Pipeline. The session handles the underlying connection and data flow.

Example Implementation:​

main.py
from videosdk.agents import AgentSession, Agent, WorkerJob, JobContext, RoomOptions
from videosdk.plugins.openai import OpenAIRealtime
from videosdk.agents import RealTimePipeline

class MyAgent(Agent):  
    def __init__(self):  
        super().__init__(instructions="You are a helpful meeting assistant.")  
  
    async def on_enter(self):  
        await self.session.say("Hello! How can I help you today?")  
      
    def setup_state_monitoring(self):  
        def on_user_state_changed(data):  
            print(f"User state changed to: {data['state']}")  
          
        def on_agent_state_changed(data):  
            print(f"Agent state changed to: {data['state']}")  
          
        self.session.on("user_state_changed", on_user_state_changed)  
        self.session.on("agent_state_changed", on_agent_state_changed)

async def start_session(ctx: JobContext):
    model = OpenAIRealtime(model="gpt-4o-realtime-preview")
    pipeline = RealTimePipeline(model=model)

    session = AgentSession(
        agent=MyAgent(),
        pipeline=pipeline
    )

    await ctx.connect()
    await session.start()
    # Session runs until manually stopped or meeting ends

def make_context():
    return JobContext(
        room_options=RoomOptions(
            room_id="your-room-id",
            auth_token="your-auth-token",
            name="Assistant Bot"
        )
    )

if __name__ == "__main__":
    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
    job.start()

Development and Testing Features​

The AgentSession supports several modes for development, testing, and user engagement:

Playground Mode​

Playground mode provides a web-based interface for testing your agent without building a separate client application.

Usage​

To activate playground mode, simply set playground: True in your RoomOptions for JobContext.

main.py
from videosdk.agents import RoomOptions, JobContext, WorkerJob

async def entrypoint(ctx: JobContext):
    # Your agent implementation here
    # This is where you create your pipeline, agent, and session
    pass

def make_context() -> JobContext:
    room_options = RoomOptions(
        room_id="<meeting_id>", 
        name="Test Agent", 
        playground=True  # Enable playground mode
    )
    
    return JobContext(room_options=room_options)

if __name__ == "__main__":
    from videosdk.agents import WorkerJob
    job = WorkerJob(entrypoint=entrypoint, jobctx=make_context)
    job.start()

When enabled, the playground URL is automatically displayed in your terminal for easy access.

note

Note: Playground mode is designed for development and testing purposes. For production deployments, ensure playground mode is disabled to maintain security and performance.

Console Mode​

Console mode allows you to test your agent directly in the terminal using your microphone and speakers, without joining a VideoSDK meeting.

Usage​

To use console mode, simply add the console argument when running your agent script:

python main.py console

The console will display:

  • Agent speech output
  • User speech input
  • Various latency metrics (STT, TTS, LLM,EOU)
  • Pipeline processing information

This flexibility allows you to use the same agent code for both development and production environments.

Session Lifecycle Management​

The AgentSession provides methods to control the agent's presence and behavior in the meeting.

start(**kwargs)

Initializes and starts the agent session. Sets up MCP tools, metrics collection, pipeline, and calls agent's on_enter() hook.

say(message: str)

Sends a message from the agent to the meeting participants. Allows the agent to communicate with users in the meeting.

close()

Gracefully shuts down the session. Finalizes metrics collection, cancels wake-up timer, and calls agent's on_exit() hook.

leave()

Leaves the meeting without full session cleanup. Provides a quick exit option while maintaining session state.

reply(instructions, wait_for_playback)

Generate agent responses using instructions and current chat context. Includes playback control and prevents concurrent calls.

interrupt()

Immediately interrupt the agent's current operation, stopping speech generation and LLM processing for emergency stops or user interruptions.

Example of Managing the Lifecycle:​

main.py
import asyncio
from videosdk.agents import AgentSession, Agent, WorkerJob, JobContext, RoomOptions
from videosdk.plugins.openai import OpenAIRealtime
from videosdk.agents import RealTimePipeline

class MyAgent(Agent):
    def __init__(self):
        super().__init__(instructions="You are a helpful meeting assistant.")

    # LIFECYCLE: Agent entry point - called when session starts
    async def on_enter(self):
        await self.session.say("Hello! How can I help you today?")

    # LIFECYCLE: Agent exit point - called when session ends
    async def on_exit(self):
        print("Agent is leaving the session")

    @function_tool  
    async def provide_summary(self) -> str:  
        """Provide a conversation summary using the new reply method"""  
        await self.session.reply("Let me summarize our conversation so far...")  
        return "Summary provided"  
      
    @function_tool    
    async def stop_speaking(self) -> str:  
        """Emergency stop functionality"""  
        await self.session.interrupt()  
        return "Agent stopped successfully"

async def run_agent_session(ctx: JobContext):
    # LIFECYCLE STAGE 1: Session Creation
    model = OpenAIRealtime(model="gpt-4o-realtime-preview")
    pipeline = RealTimePipeline(model=model)

    session = AgentSession(agent=MyAgent(), pipeline=pipeline)

    try:
        # LIFECYCLE STAGE 2: Connection Establishment
        await ctx.connect()

        # LIFECYCLE STAGE 3: Session Start
        await session.start()

        # LIFECYCLE STAGE 4: Session Running
        await asyncio.Event().wait()

    finally:
        # LIFECYCLE STAGE 5: Session Cleanup
        await session.close()

        # LIFECYCLE STAGE 6: Context Shutdown
        await ctx.shutdown()

# LIFECYCLE STAGE 0: Context Creation
def make_context() -> JobContext:
    room_options = RoomOptions(room_id="your-room-id", auth_token="your-token")
    return JobContext(room_options=room_options)

if __name__ == "__main__":
    # LIFECYCLE ORCHESTRATION: Worker Job Management
    # Creates and starts the worker job that manages the entire lifecycle
    job = WorkerJob(entrypoint=run_agent_session, jobctx=make_context)
    job.start()

Enhanced Control Methods​

The AgentSession provides advanced methods for dynamic interaction control during runtime.

Enhanced Reply System​

The reply() method allows you to generate agent responses dynamically using custom instructions while maintaining conversation context:

main.py
# Basic usage - generate immediate response  
await session.reply(instructions="Please summarize the conversation so far")  
  
# Wait for complete playback before allowing new inputs  
await session.reply(instructions="Explain the next steps", wait_for_playback=True)  
  
# Practical example in an agent  
class MyAgent(Agent):  
    async def on_enter(self):  
        await self.session.say("Hello! How can I help you today?")  
      
    @function_tool  
    async def get_summary(self) -> str:  
        # Generate a contextual summary  
        await self.session.reply(instructions="Based on our conversation, let me provide a summary...")  
        return "Summary generated"

Interrupt Control​

The interrupt() method provides immediate control to stop the agent's current operation:

main.py
# Emergency stop during agent response  
await session.interrupt()  
  
# User interruption handling  
class InteractiveAgent(Agent):  
    def __init__(self):  
        super().__init__(instructions="You are a helpful assistant.")  
        self.user_wants_to_interrupt = False  
      
    async def handle_user_input(self, user_input: str):  
        if "stop" in user_input.lower():  
            await self.session.interrupt()  
            await self.session.reply(instructions="How can I help you instead?")  
          
    @function_tool  
    async def emergency_stop(self) -> str:  
        """Stop current agent operation immediately"""  
        await self.session.interrupt()  
        return "Agent stopped successfully"

Examples - Try Out Yourself​

We have examples to get you started. Go ahead, try out, talk to agent, understand and customize according to your needs.

Wakeup call Example

Agent Session with wakeup call

Agent to agent Example

Agent Session with Customer and Loan agent

Got a Question? Ask us on discord

Last updated on by poojan
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