Build a Voice Agent for Customer Support

Create a customer support voice agent with VideoSDK. Follow our detailed guide with code and testing steps.

Introduction to AI Voice Agents in Customer Support

In today's fast-paced world, businesses are constantly seeking ways to enhance customer experience and streamline their support processes. One of the most innovative solutions is the use of AI Voice Agents. These agents are designed to interact with customers through voice, providing instant assistance and resolving queries efficiently.

What is an AI

Voice Agent

?

An AI

Voice Agent

is a sophisticated software application that uses artificial intelligence to understand and respond to human speech. These agents can perform a variety of tasks, from answering customer inquiries to providing detailed information about products and services.

Why are they important for the Customer Support Industry?

AI Voice Agents are revolutionizing the customer support industry by offering 24/7 assistance, reducing wait times, and improving customer satisfaction. They can handle a large volume of inquiries simultaneously, freeing up human agents to focus on more complex issues.

Core Components of a

Voice Agent

The core components of a

voice agent

include:
  • Speech-to-Text (STT): Converts spoken language into text.
  • Large Language Model (LLM): Processes the text to generate a response.
  • Text-to-Speech (TTS): Converts the generated text 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, we'll guide you through building a

voice agent

for customer support using the VideoSDK framework. You'll learn how to set up the environment, create a custom agent, and test it in a real-world scenario.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI Voice Agent involves several key components working together seamlessly. The process begins with capturing the user's speech, which is then converted to text using STT. The LLM processes this text to generate a suitable response, which is then converted back to speech using TTS.
Diagram

Understanding Key Concepts in the VideoSDK Framework

Setting Up the Development Environment

Prerequisites

Before we begin, ensure you have the following:
  • Python 3.11+
  • A VideoSDK account. Sign up at app.videosdk.live.

Step 1: Create a Virtual Environment

To avoid conflicts with other projects, create a virtual environment:
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

Now that the environment is set up, let's dive into building the voice agent.

Complete Code Block

Here is the complete code for the 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 friendly and efficient voice agent for customer support. Your primary role is to assist customers by answering their queries, providing information about products and services, and resolving common issues. You can handle tasks such as tracking orders, processing returns, and providing troubleshooting steps for common problems. However, you must always maintain a polite and professional tone.\n\nCapabilities:\n1. Answer customer inquiries about product details, availability, and pricing.\n2. Assist with order tracking and provide updates on delivery status.\n3. Guide customers through the process of returns and exchanges.\n4. Offer basic troubleshooting advice for common product issues.\n5. Escalate complex issues to a human representative when necessary.\n\nConstraints:\n1. You do not have access to personal customer data beyond what is provided during the interaction.\n2. You cannot process payments or handle sensitive financial information.\n3. Always include a disclaimer that complex issues may require human intervention.\n4. You must not provide legal or medical advice, and should direct customers to appropriate professionals for such inquiries."
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'll need a meeting ID. You can generate one using the VideoSDK API:
1curl -X POST "https://api.videosdk.live/v1/meetings" \
2-H "Authorization: Bearer your_api_key_here"
3

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where we define the agent's behavior. It inherits from the Agent class and implements the on_enter and on_exit methods to greet and bid farewell to 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 flow of data through the system. It consists of several plugins:
  • STT (DeepgramSTT): Converts voice to text.
  • LLM (OpenAILLM): Processes text and generates responses.
  • TTS (ElevenLabsTTS): Converts text back to voice.
  • VAD (SileroVAD): Detects when the user is speaking.
  • TurnDetector: Identifies conversational turns.
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 and sets up the session. The make_context function creates a JobContext with room options for testing.
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(
12        agent=agent,
13        pipeline=pipeline,
14        conversation_flow=conversation_flow
15    )
16    try:
17        await context.connect()
18        await session.start()
19        await asyncio.Event().wait()
20    finally:
21        await session.close()
22        await context.shutdown()
23
24def make_context() -> JobContext:
25    room_options = RoomOptions(
26        name="VideoSDK Cascaded Agent",
27        playground=True
28    )
29    return JobContext(room_options=room_options)
30
31if __name__ == "__main__":
32    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
33    job.start()
34

Running and Testing the Agent

Step 5.1: Running the Python Script

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

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, you'll receive a playground link in the console. Open this link in a browser to interact with your voice agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend your agent's functionality by integrating custom tools. This can be done by creating new plugins or modifying existing ones.

Exploring Other Plugins

While this tutorial uses specific plugins for STT, LLM, and TTS, the VideoSDK framework supports other options. Explore different plugins to find the best fit for your use case.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API key is correctly configured in the .env file. Double-check for typos or missing credentials.

Audio Input/Output Problems

Verify your microphone and speaker settings. Ensure they are properly connected and configured on your system.

Dependency and Version Conflicts

If you encounter errors related to package versions, ensure all dependencies are up-to-date. Use a virtual environment to manage package versions effectively.

Conclusion

Summary of What You've Built

Congratulations! You've built a fully functional AI Voice Agent for customer support using the VideoSDK framework. This agent can handle basic customer inquiries and provide assistance efficiently.

Next Steps and Further Learning

Consider exploring additional features and customizations to enhance your agent's capabilities. Dive deeper into the VideoSDK documentation to learn more about advanced integrations and optimizations, including

AI voice Agent Sessions

.

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