How to Build a Low-Latency Voice Agent with Rime AI
To build a low-latency voice agent with Rime AI, stream three stages — speech-to-text, LLM reasoning, and Rime’s text-to-speech — so each starts before the previous finishes. The goal is a reply that begins within 300-800ms of the user stopping.
Why Does Latency Decide Whether a Voice Agent Feels Human?
Conversation has rhythm. A pause of more than about a second reads as a broken line, and users start talking over the agent. Real humans begin responding in roughly 200ms. Everything in the pipeline competes against that number.
Faster models help, but architecture matters more. Removing a serial step beats shaving milliseconds off any single call.
What Does the Pipeline Look Like?
| Stage | Role | Latency target |
|---|---|---|
| Speech-to-text | Turn audio into text | 150-300ms |
| LLM | Decide the reply | 200-500ms to first token |
| Rime TTS | Turn text into speech | First audio <300ms |
Overlap them. Start TTS on the LLM’s first sentence rather than its last, and use end-of-turn detection that fires on silence rather than a fixed timeout.
How Do You Stream Rime’s Output?
- Request streaming audio from the TTS endpoint instead of a single file.
- Buffer a small first chunk and play as bytes arrive.
- Chunk your LLM output into sentence-sized pieces for TTS.
- Pre-generate greetings and confirmations so the first line is instant.
The first two words decide the perceived speed. Waiting for the whole reply before playing it kills the illusion no matter how fast the backend is.
How Do You Cut Cost Without Hurting Quality?
Cache everything deterministic: greetings, hours of operation, closing lines, retry prompts. That is usually 30-40% of utterances in a support flow. Use a shorter, faster LLM for simple intents and reserve the bigger model for genuinely complex turns. And cap the reply length — agents that talk for twenty seconds lose callers.
How Do You Handle Interruptions?
People cut in. Detect speech during playback and stop the audio immediately, otherwise the agent talks over the user, which feels worse than any delay. After an interruption, discard the unplayed text and re-enter the reasoning loop with the new input.
What Should You Test Before Launch?
Measure round-trip latency on real networks, not localhost — add 100-200ms for mobile. Run 50 calls with varied accents and background noise. Then listen for the hard cases: phone numbers, names and addresses, where Rime’s pacing cues matter most. An agent that sounds natural 90% of the time and garbles digits will still frustrate callers.
Quick Summary
- Stream STT, LLM and Rime TTS so stages overlap, not queue.
- Target first audio within 300-800ms of the user finishing.
- Send sentence-sized chunks to TTS; pre-generate greetings.
- Cache deterministic utterances and cap reply length to control cost.
- Handle barge-in by stopping audio instantly and re-running reasoning.
