Build an AI Voice Agent with VideoSDK

Step-by-step guide to building an AI Voice Agent using VideoSDK, complete with code examples and testing instructions.

Introduction to AI Voice Agents in voice agent sdk

AI Voice Agents are rapidly transforming how we interact with technology. These agents are software programs designed to understand and respond to human speech, providing a seamless interface for users. In the context of the voice agent sdk, these agents are crucial for enabling voice-driven applications across various industries.

What is an AI Voice Agent?

An AI Voice Agent is a digital assistant that uses artificial intelligence to process and respond to human speech. It leverages technologies such as speech-to-text (STT), language models (LLM), and text-to-speech (TTS) to facilitate natural language interactions.

Why are they important for the voice agent sdk industry?

In industries like customer service, healthcare, and smart home technology, AI Voice Agents enhance user experience by providing hands-free, real-time assistance. They automate routine tasks, improve accessibility, and enable more intuitive user interfaces.

Core Components of a Voice Agent

  • Speech-to-Text (STT): Converts spoken language into text.
  • Language Model (LLM): Processes the text to understand and generate responses.
  • Text-to-Speech (TTS): Converts text responses back into spoken language.

What You'll Build in This Tutorial

In this tutorial, you will

build a fully functional AI Voice Agent using the VideoSDK

 framework. This agent will be capable of understanding and responding to user queries in real-time.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI Voice Agent involves several components working together to process user input and generate responses. The data flow begins with the user's speech, which is converted to text by the STT module. The text is then processed by the LLM, and a response is generated. Finally, the TTS module converts this response back into speech.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, responsible for handling interactions.
  • CascadingPipeline: Manages the flow of audio processing, from STT to LLM to 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.

Setting Up the Development Environment

Prerequisites

To get started, ensure you have Python 3.11+ installed and a VideoSDK account. You can sign up at app.videosdk.live.

Step 1: Create a Virtual Environment

Create a virtual environment to manage your project dependencies:
1python -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
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 a 'Voice Agent SDK' specialist, designed to assist developers in implementing voice-enabled applications using the VideoSDK framework. Your persona is that of a knowledgeable and friendly technical assistant. Your capabilities include providing detailed guidance on integrating the SDK into various platforms, explaining features and functionalities, and troubleshooting common issues developers might encounter. You can also offer best practices for optimizing voice interactions and ensuring seamless user experiences. However, you are not a substitute for official technical support or documentation, and you must remind users to refer to the official VideoSDK documentation for comprehensive technical details and updates. Additionally, you cannot provide code snippets for unsupported programming languages or platforms."
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 create a meeting ID, use the following curl command:
1curl -X POST https://api.videosdk.live/v1/meetings -H "Authorization: YOUR_API_KEY" -H "Content-Type: application/json" -d '{}'
2

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where you define the agent's behavior. It inherits from the Agent class and specifies actions on entering and exiting a session:
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 crucial for processing audio data. It integrates various plugins for STT, LLM, TTS, VAD, and turn detection. You can explore the

Deepgram STT Plugin for voice agent

,

OpenAI LLM Plugin for voice agent

, and

ElevenLabs TTS Plugin for voice agent

 for enhanced functionality.
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 manages the lifecycle of the agent's session, while the make_context function sets up the environment. The

AI voice Agent Sessions

 are crucial for maintaining the flow of interaction.
1async def start_session(context: JobContext):
2    agent = MyVoiceAgent()
3    conversation_flow = ConversationFlow(agent)
4    pipeline = CascadingPipeline(
5        stt=DeepgramSTT(model="nova-2", language="en"),
6        llm=OpenAILLM(model="gpt-4o"),
7        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
8        vad=SileroVAD(threshold=0.35),
9        turn_detector=TurnDetector(threshold=0.8)
10    )
11    session = AgentSession(agent=agent, pipeline=pipeline, conversation_flow=conversation_flow)
12    try:
13        await context.connect()
14        await session.start()
15        await asyncio.Event().wait()
16    finally:
17        await session.close()
18        await context.shutdown()
19
20def make_context() -> JobContext:
21    room_options = RoomOptions(
22        name="VideoSDK Cascaded Agent",
23        playground=True
24    )
25    return JobContext(room_options=room_options)
26
27if __name__ == "__main__":
28    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
29    job.start()
30

Running and Testing the Agent

Step 5.1: Running the Python Script

To run your agent, execute the following command in your terminal:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

After starting the agent, find the

AI Agent playground

 link in the console output. Use this link to join the session and interact with your agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend the agent's capabilities by integrating custom tools using the function_tool feature of the VideoSDK framework.

Exploring Other Plugins

Explore other plugins for STT, LLM, and TTS to customize your agent's performance and capabilities. Consider using

Silero Voice Activity Detection

 and the

Turn detector for AI voice Agents

 for improved interaction management.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API key is correctly set in the .env file and matches your VideoSDK account.

Audio Input/Output Problems

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

Dependency and Version Conflicts

Ensure all dependencies are installed with compatible versions by checking the requirements.txt file.

Conclusion

Summary of What You've Built

In this tutorial, you've built an AI Voice Agent using the VideoSDK framework, capable of real-time voice interactions.

Next Steps and Further Learning

Explore additional features of the VideoSDK framework and experiment with different plugins to enhance your agent.

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