Build an AI Voice Agent for Education

Step-by-step guide to building an AI Voice Agent for education using VideoSDK with complete code examples.

Introduction to AI Voice Agents in Education

In recent years, AI Voice Agents have become an integral part of various industries, including education. These intelligent systems can interpret human speech, process it, and respond in a way that mimics human conversation. This tutorial will guide you through building an AI

Voice Agent

 specifically designed for the education sector using the VideoSDK framework.

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a software application that uses artificial intelligence to interact with users through voice commands. It processes spoken language, understands it, and provides a suitable response, often through text-to-speech (TTS) systems. These agents are powered by technologies like Speech-to-Text (STT), Language Learning Models (LLM), and TTS.

Why are they important for the education industry?

In education, AI Voice Agents can serve multiple purposes. They can assist students with homework, provide explanations of complex topics, and offer study tips. Teachers can use them to automate administrative tasks or provide additional resources to students. Parents can benefit from reminders about school events or deadlines.

Core Components of a

Voice Agent

  1. STT (Speech-to-Text): Converts spoken language into text.
  2. LLM (Language Learning Model): Processes the text to understand and generate responses.
  3. TTS (Text-to-Speech): Converts text responses back into speech.
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 a fully functional AI

Voice Agent

 for educational purposes. We will walk you through setting up the development environment, creating the agent, and testing it using VideoSDK.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI Voice Agent involves several components working together seamlessly. The process begins with capturing user speech, converting it to text, processing the text to generate a response, and finally converting the response back to speech.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot. It handles interactions and manages the conversation flow.
  • CascadingPipeline: This defines the flow of audio processing, starting with STT, moving through LLM, and ending with TTS. 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. For more details, explore the

    Turn detector for AI voice Agents

    .

Setting Up the Development Environment

Prerequisites

Before we start, 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

To keep dependencies organized, create a virtual environment:
1python3 -m venv venv
2source venv/bin/activate  # On Windows use `venv\Scripts\activate`
3

Step 2: Install Required Packages

Install the necessary packages using pip:
1pip install videosdk-agents videosdk-plugins
2

Step 3: Configure API Keys in a .env File

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

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

Here is the complete code for building your AI Voice Agent:
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 designed specifically for the education sector. Your primary role is to assist students, teachers, and parents by providing educational support and resources. You can answer questions related to various subjects, provide explanations of complex topics, and offer study tips and resources. Additionally, you can help schedule study sessions and remind users of important deadlines. However, you are not a certified educator, and your responses should always encourage users to verify information with their teachers or educational institutions. You must include a disclaimer that your assistance is supplementary and not a replacement for professional educational guidance. Your interactions should be friendly, supportive, and informative, aiming to enhance the learning experience for all users."
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 your agent, you need a meeting ID. You can generate this using the VideoSDK API:
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 where you define the behavior of your agent. It inherits from the Agent class and uses the agent_instructions to guide its interactions. The on_enter and on_exit methods are used to provide greetings and farewells.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is crucial as it defines how the agent processes audio. It includes:
  • DeepgramSTT: Converts speech to text.
  • OpenAILLM: Processes the text to generate responses.
  • ElevenLabsTTS: Converts responses back to speech.
  • SileroVAD & TurnDetector: Manage when the agent listens and speaks. For more on voice activity detection, see

    Silero Voice Activity Detection

    .

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session and starts the conversation flow. The make_context function sets up the room options, enabling the playground mode for testing. The if __name__ == "__main__": block ensures that the script runs as a standalone program.

Running and Testing the Agent

Step 5.1: Running the Python Script

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

Step 5.2: Interacting with the Agent in the Playground

Once the agent is running, you will find a playground link in the console. Use this link to join the session and interact with your agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend your agent's capabilities by integrating custom tools. This allows the agent to perform specific tasks beyond the standard setup.

Exploring Other Plugins

VideoSDK supports various plugins for STT, LLM, and TTS. Explore options like Cartesia for STT or Google Gemini for LLM to enhance your agent's capabilities.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly set in the .env file. Double-check for typos or missing keys.

Audio Input/Output Problems

Verify your microphone and speaker settings. Ensure the correct devices are selected and functioning.

Dependency and Version Conflicts

Use a virtual environment to manage dependencies and prevent version conflicts. Ensure all packages are up-to-date.

Conclusion

Summary of What You've Built

You've successfully built an AI Voice Agent for education using VideoSDK. This agent can assist students, teachers, and parents with educational tasks.

Next Steps and Further Learning

Consider exploring more advanced features or integrating additional plugins to expand your agent's capabilities. Stay updated with the latest developments in AI and voice technology. For more on managing sessions, explore

AI voice Agent Sessions

.

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