LLM Examples for Voice Agent: Practical Guides & Code (2025)

Introduction to LLM Examples for Voice Agent

What is a Voice Agent Powered by LLM?

• Customer Support: Automate natural conversations for troubleshooting, booking, and inquiries.
• Smart Assistants: Power home automation, scheduling, and information retrieval.
• Accessibility Tools: Enable voice-driven navigation and content consumption for diverse users.

Key Components of LLM-Based Voice Agents

Speech-to-Text (STT)

Large Language Model (LLM)

Text-to-Speech (TTS)

State Management and Memory

Architecture Diagram

Popular LLMs and Frameworks for Voice Agents

• Llama 3: Open-source, efficient, and highly customizable for private deployments.
• GPT (OpenAI): Industry-leading conversational capabilities, available via API.
• DiVA Llama 3: A fine-tuned Llama 3 variant designed for dialogue and voice applications.

• Open-source LLMs (like Llama 3) offer transparency, self-hosting, and community-driven improvements.
• Commercial APIs (such as OpenAI's GPT) provide ease of use, scalability, and robust support.

• Rasa Open Source

• Picovoice

• Coqui AI

• OpenAI Whisper (STT)

• Mozilla TTS

• For rapid deployment of video-enabled voice agents, consider using an

  embed video calling sdk

  to integrate prebuilt communication modules directly into your app.

Practical LLM Examples for Voice Agents

Example 1: Simple Conversational Voice Agent (TypeScript)

1// Import required modules
2import { transcribeAudio } from "./stt";
3import { queryLLM } from "./llm";
4import { synthesizeSpeech } from "./tts";
5
6async function voiceAgent(audioInput: Buffer): Promise<void> {
7    // Step 1: Convert speech to text
8    const userText = await transcribeAudio(audioInput);
9
10    // Step 2: Send text to LLM for response
11    const llmResponse = await queryLLM(userText);
12
13    // Step 3: Convert response to speech
14    const speechOutput = await synthesizeSpeech(llmResponse);
15
16    // Step 4: Play synthesized speech (platform-dependent)
17    playAudio(speechOutput);
18}
19

Example 2: Stateful RPG Interaction (Pocket Computer, TypeScript)

1// Memory object to track conversation state
2let conversationState = {
3    character: "AI Dungeon Master",
4    quest: "Find the lost artifact",
5    inventory: ["map"],
6};
7
8function buildPrompt(userInput: string): string {
9    return `You are ${conversationState.character}. The quest is: ${conversationState.quest}. Inventory: ${conversationState.inventory.join(", ")}.\nUser says: ${userInput}\nRespond as an RPG guide.`;
10}
11
12async function rpgVoiceAgent(audioInput: Buffer): Promise<void> {
13    const userText = await transcribeAudio(audioInput);
14    const prompt = buildPrompt(userText);
15    const llmResponse = await queryLLM(prompt);
16    const speechOutput = await synthesizeSpeech(llmResponse, {
17        personality: "mysterious",
18        accent: "british"
19    });
20    playAudio(speechOutput);
21}
22

Deployment Reference: Setting up Your Voice Agent

1// Example: Express.js endpoint for voice agent
2app.post("/voice-agent", async (req, res) => {
3    const audioInput = req.body.audio;
4    await voiceAgent(audioInput);
5    res.send({ status: "ok" });
6});
7

• Modify the synthesizeSpeech function to select voices, accents, and tones for unique personalities.
• Use prompt engineering to tune your agent's behavior and context retention.
• For voice-enabled group interactions, integrating a

  Voice SDK

  can enable scalable live audio rooms and enhance conversational experiences.

Best Practices for Building LLM Voice Agents

Data Privacy

• Use anonymization and encryption for user data.
• Comply with GDPR, CCPA, and other relevant regulations.

Latency and Efficiency

• Minimize inference time by caching frequent responses and using on-device LLMs when possible.
• Optimize STT and TTS pipeline latency for real-time interactions.
• For applications requiring telephony integration, a

  phone call api

  can help you manage calls efficiently and reliably.

Prompt Engineering for Voice Agents

1const prompt = `You are a helpful voice assistant. If the user mentions "order status", ask for the order number.\nUser: ${userInput}\nAssistant:`;
2

Voice UX and Accessibility

• Offer multiple voice personalities and languages.
• Provide visual feedback for speech input and output.
• Ensure compatibility with assistive technologies.
• For seamless integration of live audio features, consider leveraging a

  Voice SDK

  to enhance accessibility and user engagement.

Challenges and Limitations

• Context Limitations: LLMs may lose context over long conversations or complex topics.
• Environmental Impact: Training and running large models require significant computational resources.
• Error Handling: LLMs can occasionally generate hallucinated or irrelevant responses. Implement fallback strategies and monitor outputs.

Future Trends in LLM Voice Agents

Conclusion