8 Leading Platforms for Building Low-Latency Voice AI Agents

The 8 Best Platforms To Build Voice AI Agents

Voice agents utilize local or cloud-based LLMs to provide human-like audio responses in real-time. Modern platforms leverage Model Context Protocol (MCP) to retrieve accurate data from services like Perplexity and Exa.

Why This Matters

Traditional voice assistants often fail at complex reasoning and lack access to real-time web search tools, frequently handing off difficult queries to external models like ChatGPT. While modern SDKs provide low-latency frameworks, developers still face technical hurdles in handling noisy environments and ensuring seamless user interruptions without breaking the conversational flow.

Key Insights

• Stream Python AI SDK integrates WebRTC and OpenAI Realtime API to provide low-latency communication for meeting bots.
• OpenAI Agents SDK offers a library of nine distinct TTS voices including Alloy, Ash, Coral, and Shimmer.
• ElevenLabs Eleven V3 model enables realistic and expressive text-to-speech for gaming and marketplace applications.
• Vapi supports multilingual operations across 100+ languages and integrates with Salesforce, Slack, and Google Calendar.
• Pipecat serves as an open-source framework for building complex dialog systems and multimodal video meeting assistants.
• Cartesia API provides Sonic and Ink-Whisper models for high-quality speech-to-text and text-to-speech in 15+ languages.

Working Examples

Initializing an OpenAI speech-to-speech pipeline using the Stream Python AI SDK.

from getstream import Stream; client = Stream.from_env(); sts_bot = OpenAIRealtime(model='gpt-4o-realtime-preview', instructions='You are a friendly assistant', voice='alloy'); async with await sts_bot.connect(call, agent_user_id=bot_user_id) as connection: await sts_bot.send_user_message('Greeting.')

Connecting a microphone and audio output via WebRTC using the OpenAI JS SDK.

import { RealtimeAgent, RealtimeSession } from '@openai/agents/realtime'; const agent = new RealtimeAgent({ name: 'Assistant', instructions: 'Helpful assistant.' }); const session = new RealtimeSession(agent); await session.connect({ apiKey: '<client-api-key>' });

Practical Applications

• Enterprise Inbound Sales: Using voice agents to follow up with leads and contact potential customers. Pitfall: Poor noise detection causing agents to misinterpret background sounds as user commands.
• Telehealth Data Collection: Implementing AI voices to interact with patients and collect medical information. Pitfall: High latency in speech-to-speech interactions disrupting the flow of clinical data gathering.
• Automated Appointment Scheduling: Integrating voice systems with browser agents for online bookings. Pitfall: Lack of robust interruption handling preventing users from correcting the agent mid-sentence.

References:

• https://dev.to/getstreamhq/the-8-best-platforms-to-build-voice-ai-agents-4oel