Why You Shouldn’t Build Real-Time Voice Agents Directly on Model APIs

Real-time or streaming APIs from major model providers like OpenAI and Google offer appealing simplicity for voice agent development, but this simplicity quickly becomes complex when building for real users. While these APIs handle AI inference, developers are left to manage crucial aspects such as audio transport, echo cancellation, turn detection, client SDKs, and scaling infrastructure, which are essential for functional voice interactions. Direct connections to model APIs often result in issues like latency, poor turn-taking, and echo, especially in real-world scenarios with fluctuating network conditions. A voice agent framework, such as LiveKit, addresses these challenges by providing WebRTC transport, built-in echo cancellation, advanced turn detection, and comprehensive client SDKs, enabling seamless integration and flexibility across different models and languages. While direct API use may suffice for quick prototypes, production environments benefit from the robust infrastructure and flexibility that a voice agent framework offers, allowing teams to focus on the unique aspects of their agents without being bogged down by system maintenance and integration challenges.

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