Using LLMs for AI Voice Agents

Step-by-step guide to build AI Voice Agents using LLMs with VideoSDK.

Introduction to AI Voice Agents in how to use LLMs for voice agents

AI Voice Agents are sophisticated systems designed to interact with users through voice commands. They leverage advanced technologies like Speech-to-Text (STT), Text-to-Speech (TTS), and Large Language Models (LLMs) to understand and respond to user queries. In the context of using LLMs for voice agents, these agents become even more powerful, offering nuanced and contextually relevant interactions.

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a software program capable of understanding and responding to voice commands. It processes spoken language input, interprets the meaning, and generates appropriate verbal responses. This technology is increasingly prevalent in applications like virtual assistants, customer service bots, and interactive voice response systems.

Why are they important for the how to use LLMs for voice agents industry?

Incorporating LLMs into voice agents enhances their ability to process complex language inputs and generate more human-like responses. This capability is crucial in industries such as customer support, where understanding context and providing accurate information is paramount.

Core Components of a

Voice Agent

  • Speech-to-Text (STT): Converts spoken language into text.
  • Large Language Models (LLM): Processes text to understand and generate responses.
  • Text-to-Speech (TTS): Converts text responses back into spoken language.
For a comprehensive understanding, refer to the

AI voice Agent core components overview

.

What You'll Build in This Tutorial

In this tutorial, you will build an AI

Voice Agent

 using the VideoSDK framework, integrating STT, LLM, and TTS components to create a responsive and intelligent voice interaction system.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI

Voice Agent

 involves a seamless flow of data from user input to agent response. The process begins with capturing user speech, converting it to text, processing it through an LLM, and finally converting the response back to speech.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, responsible for managing interactions.
  • CascadingPipeline: A structured flow of audio processing that involves STT, LLM, and TTS components. Learn more about the

    Cascading pipeline in AI voice Agents

    .
  • VAD & TurnDetector: These components help the agent determine when to listen and when to speak, ensuring smooth interactions.

Setting Up the Development Environment

Prerequisites

Before you begin, ensure you have Python 3.11+ installed and a VideoSDK account, which you can create at app.videosdk.live.

Step 1: Create a Virtual Environment

Create a virtual environment to manage your project dependencies:
1python -m venv voice-agent-env
2source voice-agent-env/bin/activate  # On Windows use `voice-agent-env\Scripts\activate`
3

Step 2: Install Required Packages

Install the necessary packages using pip:
1pip install videosdk
2

Step 3: Configure API Keys in a .env file

Create a .env file in your project directory and add your VideoSDK API key:
1VIDEOSDK_API_KEY=your_api_key_here
2

Building the AI Voice Agent: A Step-by-Step Guide

Below is the complete runnable code for the AI Voice Agent. We will break it down into smaller parts for detailed explanations in the following sections.
1import asyncio, os
2from videosdk.agents import Agent, AgentSession, CascadingPipeline, JobContext, RoomOptions, WorkerJob, ConversationFlow
3from videosdk.plugins.silero import SileroVAD
4from videosdk.plugins.turn_detector import TurnDetector, pre_download_model
5from videosdk.plugins.deepgram import DeepgramSTT
6from videosdk.plugins.openai import OpenAILLM
7from videosdk.plugins.elevenlabs import ElevenLabsTTS
8from typing import AsyncIterator
9
10# Pre-downloading the Turn Detector model
11pre_download_model()
12
13agent_instructions = "You are an AI Voice Agent specializing in guiding users on 'how to use LLMs for voice agents'. Your persona is that of a knowledgeable and friendly tech consultant. Your primary capabilities include explaining the basics of Large Language Models (LLMs), providing step-by-step guidance on integrating LLMs into voice agents, and offering best practices for optimizing performance. You can also answer common questions related to LLMs and voice agent technology. However, you must clarify that you are not a certified AI engineer and advise users to consult professional developers for complex implementation issues. Always ensure that your responses are clear, concise, and user-friendly, avoiding overly technical jargon unless necessary for clarity."
14
15class MyVoiceAgent(Agent):
16    def __init__(self):
17        super().__init__(instructions=agent_instructions)
18    async def on_enter(self): await self.session.say("Hello! How can I help?")
19    async def on_exit(self): await self.session.say("Goodbye!")
20
21async def start_session(context: JobContext):
22    # Create agent and conversation flow
23    agent = MyVoiceAgent()
24    conversation_flow = ConversationFlow(agent)
25
26    # Create pipeline
27    pipeline = CascadingPipeline(
28        stt=DeepgramSTT(model="nova-2", language="en"),
29        llm=OpenAILLM(model="gpt-4o"),
30        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
31        vad=SileroVAD(threshold=0.35),
32        turn_detector=TurnDetector(threshold=0.8)
33    )
34
35    session = AgentSession(
36        agent=agent,
37        pipeline=pipeline,
38        conversation_flow=conversation_flow
39    )
40
41    try:
42        await context.connect()
43        await session.start()
44        # Keep the session running until manually terminated
45        await asyncio.Event().wait()
46    finally:
47        # Clean up resources when done
48        await session.close()
49        await context.shutdown()
50
51def make_context() -> JobContext:
52    room_options = RoomOptions(
53    #  room_id="YOUR_MEETING_ID",  # Set to join a pre-created room; omit to auto-create
54        name="VideoSDK Cascaded Agent",
55        playground=True
56    )
57
58    return JobContext(room_options=room_options)
59
60if __name__ == "__main__":
61    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
62    job.start()
63

Step 4.1: Generating a VideoSDK Meeting ID

To interact with the agent, you need a meeting ID. You can generate one using the VideoSDK API with a curl command:
1curl -X POST "https://api.videosdk.live/v1/meetings" \
2-H "Authorization: Bearer YOUR_API_KEY" \
3-H "Content-Type: application/json"
4

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is the heart of your voice agent, where you define its behavior and responses. This class inherits from the Agent class provided by the VideoSDK framework.
1class MyVoiceAgent(Agent):
2    def __init__(self):
3        super().__init__(instructions=agent_instructions)
4    async def on_enter(self): await self.session.say("Hello! How can I help?")
5    async def on_exit(self): await self.session.say("Goodbye!")
6

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is where you define the flow of data through your agent, specifying the STT, LLM, TTS, VAD, and Turn Detector components. For enhanced STT capabilities, consider using the

Deepgram STT Plugin for voice agent

.
1pipeline = CascadingPipeline(
2    stt=DeepgramSTT(model="nova-2", language="en"),
3    llm=OpenAILLM(model="gpt-4o"),
4    tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
5    vad=SileroVAD(threshold=0.35),
6    turn_detector=TurnDetector(threshold=0.8)
7)
8

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session and manages the interaction lifecycle. The make_context function sets up the environment for the agent to operate. For more insights, explore

AI voice Agent Sessions

.
1async def start_session(context: JobContext):
2    # Create agent and conversation flow
3    agent = MyVoiceAgent()
4    conversation_flow = ConversationFlow(agent)
5
6    # Create pipeline
7    pipeline = CascadingPipeline(
8        stt=DeepgramSTT(model="nova-2", language="en"),
9        llm=OpenAILLM(model="gpt-4o"),
10        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
11        vad=SileroVAD(threshold=0.35),
12        turn_detector=TurnDetector(threshold=0.8)
13    )
14
15    session = AgentSession(
16        agent=agent,
17        pipeline=pipeline,
18        conversation_flow=conversation_flow
19    )
20
21    try:
22        await context.connect()
23        await session.start()
24        # Keep the session running until manually terminated
25        await asyncio.Event().wait()
26    finally:
27        # Clean up resources when done
28        await session.close()
29        await context.shutdown()
30

Running and Testing the Agent

Step 5.1: Running the Python Script

To run your agent, execute the Python script:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the agent is running, you will receive a playground link in the console. Use this link to interact with your agent and test its capabilities.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend your agent's functionality with custom tools, enhancing its capabilities.

Exploring Other Plugins

Consider exploring other plugins for STT, LLM, and TTS to tailor the agent's performance to your specific needs. For instance, the

Silero Voice Activity Detection

 plugin can improve the agent's ability to detect when to listen and respond.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly configured in the .env file and that they have the necessary permissions.

Audio Input/Output Problems

Check your microphone and speaker settings to ensure they are correctly configured and functioning.

Dependency and Version Conflicts

Ensure all dependencies are installed with compatible versions to avoid conflicts during execution.

Conclusion

Summary of What You've Built

In this tutorial, you have built a fully functional AI Voice Agent using LLMs and the VideoSDK framework, capable of understanding and responding to user queries.

Next Steps and Further Learning

Explore advanced features and customizations to enhance your agent's capabilities and consider integrating it into real-world applications.

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