Build an AI Voice Agent for Logistics

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

Introduction to AI Voice Agents in the Logistics Industry

AI Voice Agents are intelligent systems designed to interact with users through natural language processing, allowing for seamless voice-based communication. These agents are particularly beneficial in industries like logistics, where real-time information and efficient communication are crucial.
In the logistics industry, AI Voice Agents can streamline operations by assisting with shipment tracking, inventory management, and delivery scheduling. They can provide logistics managers and staff with quick access to information, thereby enhancing decision-making and operational efficiency.

Core Components of a Voice Agent

To build a robust AI Voice Agent, three core components are essential:
  • Speech-to-Text (STT): Converts spoken language into text.
  • Large Language Model (LLM): Processes the text to understand and generate responses.
  • Text-to-Speech (TTS): Converts the response text back into spoken language.
For a detailed 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 the process of building an AI Voice Agent tailored for the logistics industry using the VideoSDK framework. You'll learn how to set up the development environment, create a custom agent, and test it in a playground environment. Start with the

Voice Agent Quick Start Guide

 for initial setup instructions.

Architecture and Core Concepts

To understand how our AI Voice Agent operates, let's explore its high-level architecture. The agent listens to user input, processes it using a

cascading pipeline in AI voice Agents

, and responds appropriately.
1sequenceDiagram
2    participant User
3    participant Agent
4    participant STT
5    participant LLM
6    participant TTS
7    User->>Agent: Speak
8    Agent->>STT: Convert Speech to Text
9    STT->>LLM: Process Text
10    LLM->>TTS: Generate Response
11    TTS->>Agent: Convert Text to Speech
12    Agent->>User: Respond
13

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class that represents your AI Voice Agent. It handles interactions and manages the conversation flow.
  • CascadingPipeline: A sequence of processes that handle audio input, language processing, and audio output.
  • VAD & TurnDetector: These components help the agent detect when to listen and when to speak, ensuring smooth interactions.
Explore the

Turn detector for AI voice Agents

 for more information on managing conversation flow.

Setting Up the Development Environment

Before we begin building our AI Voice Agent, we need to set up the development environment.

Prerequisites

Ensure you have Python 3.11+ installed and create an account on VideoSDK at app.videosdk.live.

Step 1: Create a Virtual Environment

Open your terminal and run the following commands to create and activate 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 Python 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

Let's dive into building our AI Voice Agent. Below is the complete code that we'll break down into smaller parts for better understanding.
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 knowledgeable logistics assistant AI Voice Agent designed to support the logistics industry. Your primary role is to assist logistics managers and staff by providing accurate and timely information related to logistics operations. You can answer questions about shipment tracking, inventory management, delivery schedules, and logistics optimization strategies. You are capable of integrating with existing logistics software to provide real-time updates and insights. However, you are not a human logistics expert and must always advise users to consult with a logistics professional for critical decisions. You must ensure data privacy and comply with industry regulations when handling sensitive information."
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 AI Voice Agent, you'll need a meeting ID. Use the following curl command to generate one:
1curl -X POST \
2  https://api.videosdk.live/v1/meetings \
3  -H "Authorization: Bearer YOUR_API_KEY"
4

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where we define the behavior of our AI Voice Agent. It inherits from the Agent class and provides custom responses when the agent enters or exits 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 the audio input and generating appropriate responses. It consists of several plugins:
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, connecting it to the VideoSDK environment. This function also ensures that the session remains active until manually terminated.
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
The make_context function sets up the environment for the agent, including the creation of a playground room for testing.
1def make_context() -> JobContext:
2    room_options = RoomOptions(
3        name="VideoSDK Cascaded Agent",
4        playground=True
5    )
6    return JobContext(room_options=room_options)
7
Finally, the main block starts the agent job.
1if __name__ == "__main__":
2    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
3    job.start()
4

Running and Testing the Agent

Step 5.1: Running the Python Script

Execute the script using the following command:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, find the playground link in the console output. Use this link to join the session and interact with your AI Voice Agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows for the integration of custom tools to extend the agent's capabilities. This can include additional data processing or integration with third-party services.

Exploring Other Plugins

While we used specific plugins for STT, LLM, and TTS, the VideoSDK framework supports various options. Explore other plugins to find ones that best suit your needs.

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 values.

Audio Input/Output Problems

Verify that your microphone and speaker settings are correctly configured. Check permissions and hardware connections.

Dependency and Version Conflicts

Ensure all dependencies are installed with compatible versions. Use a virtual environment to manage dependencies effectively.

Conclusion

In this tutorial, you've built a fully functional AI Voice Agent tailored for the logistics industry using the VideoSDK framework. You've learned how to set up the environment, create a custom agent, and test it in a playground.
As next steps, consider exploring additional plugins and features to further enhance your agent's capabilities. Continue learning and experimenting to build more sophisticated voice-based solutions.
For more detailed instructions, refer to the

AI voice Agent Sessions

 documentation.

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