Build an Omnichannel Conversational AI Voice Agent

Implement an omnichannel conversational AI voice agent with VideoSDK. Follow this step-by-step guide with complete code examples.

Introduction to AI Voice Agents in Omnichannel Conversational AI

AI Voice Agents are revolutionizing the way businesses interact with customers by providing seamless, automated communication across various channels. In this tutorial, we will explore the implementation of an omnichannel conversational AI

voice agent

using VideoSDK's framework, which integrates speech-to-text (STT), text-to-speech (TTS), and language models (LLM) to create a robust conversational experience.

What is an AI

Voice Agent

?

An AI

Voice Agent

is a software solution designed to simulate human-like conversations through voice interactions. These agents can understand spoken language, process the information, and respond appropriately, making them ideal for customer support, virtual assistants, and more.

Why are they important for the Omnichannel Conversational AI Industry?

In the omnichannel conversational AI industry, voice agents play a crucial role by enabling businesses to offer consistent and efficient customer service across multiple platforms, such as phone, chat, and email. This ensures that users receive the same quality of service, regardless of the communication channel.

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 the generated text response 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 guide, you'll learn to build a fully functional AI

voice agent

capable of interacting with users across various channels, using VideoSDK's powerful framework.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI voice agent involves a seamless flow of data from user speech to agent response. The process starts with capturing the user's voice input, which is then converted into text using STT. The text is processed by an LLM to generate a response, which is finally converted back into speech using TTS.
Diagram

Understanding Key Concepts in the VideoSDK Framework

Setting Up the Development Environment

Prerequisites

Before you begin, 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 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 key:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Here is the complete code to build 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 omnichannel conversational AI agent designed to provide seamless customer support across multiple platforms, including voice, chat, and email. Your primary role is to assist users by answering queries, providing product information, and guiding them through troubleshooting processes. You are capable of understanding and responding to user inquiries in a natural and conversational manner, ensuring a consistent experience across all channels.\n\nCapabilities:\n1. Respond to customer inquiries about products and services.\n2. Provide step-by-step troubleshooting assistance.\n3. Offer personalized recommendations based on user preferences and history.\n4. Seamlessly switch between different communication channels while maintaining context.\n5. Collect and analyze customer feedback to improve service quality.\n\nConstraints and Limitations:\n1. You are not authorized to process payments or handle sensitive financial information.\n2. You must always include a disclaimer that complex issues may require human intervention.\n3. You cannot provide legal or medical advice and should direct users to consult professionals for such inquiries.\n4. Ensure user privacy and data protection by adhering to relevant regulations and guidelines.\n5. Maintain a neutral and professional tone, avoiding any form of bias or discrimination."
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 one using the following curl command:
1curl -X POST \
2  https://api.videosdk.live/v1/meetings \
3  -H "Authorization: Bearer YOUR_API_KEY" \
4  -H "Content-Type: application/json"
5

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the Agent class, defining how the agent should behave when entering and exiting a session. The on_enter and on_exit methods provide initial and final interactions with the user.
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 as it defines the sequence of processing stages: STT, LLM, TTS, VAD, and TurnDetector. Each plugin plays a specific role in transforming and managing the data flow.
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 session, connects to the context, and manages the lifecycle of the agent. The make_context function sets up the room options for testing.
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
31def make_context() -> JobContext:
32    room_options = RoomOptions(
33    #  room_id="YOUR_MEETING_ID",  # Set to join a pre-created room; omit to auto-create
34        name="VideoSDK Cascaded Agent",
35        playground=True
36    )
37
38    return JobContext(room_options=room_options)
39
40if __name__ == "__main__":
41    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
42    job.start()
43

Running and Testing the Agent

Step 5.1: Running the Python Script

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

Step 5.2: Interacting with the Agent in the Playground

Once the agent is running, use the playground link provided in the console to interact with the agent. You can speak to the agent and receive responses in real-time.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend the agent's capabilities by integrating custom tools. This enables you to tailor the agent to specific business needs.

Exploring Other Plugins

While this tutorial uses specific plugins, VideoSDK supports various STT, LLM, and TTS options. Explore other plugins to enhance the agent's functionality.

Troubleshooting Common Issues

API Key and Authentication Errors

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

Audio Input/Output Problems

Verify that your microphone and speakers are working correctly. Check the system settings and permissions.

Dependency and Version Conflicts

Ensure all dependencies are compatible with Python 3.11+. Use a virtual environment to manage package versions.

Conclusion

Summary of What You've Built

In this tutorial, you've built a fully functional omnichannel conversational AI voice agent using VideoSDK. This agent can interact with users across various platforms, providing a seamless and consistent experience.

Next Steps and Further Learning

Explore additional features and plugins offered by VideoSDK to further enhance your agent's capabilities. Consider integrating more advanced AI models and custom tools to meet specific business requirements. For more insights into managing sessions, refer to

AI voice Agent Sessions

.

Start Building With Free $20 Balance

No credit card required to start.

Want to level-up your learning? Subscribe now

Subscribe to our newsletter for more tech based insights

FAQ