Introduction

If you add a realtime avatar to a voice agent, the first user-visible problem is usually not the model or the lip sync itself. It’s the cold start: the pause between “user started speaking” and “avatar is actually on screen, connected, and ready to render the first frame.” That delay is enough to make a demo feel sluggish and a production agent feel unresponsive.

This post is about reducing that first-frame delay by warming sessions in advance. By the end, you should be able to reason about where the latency comes from, separate the parts you can precompute from the parts you can’t, and design your integration so the avatar session is ready before the user sees it.

Where the delay comes from

For a realtime avatar app, “first frame” is not one event. It’s usually the end of a short chain:

• session creation on the backend

• authentication and authorization

• media pipeline setup

• WebRTC signaling and ICE connectivity checks

• model or rendering warmup

• first audio/video packets flowing to the browser

Some of these are network-bound, some are compute-bound, and some are just orchestration overhead. The important part is that the user experiences the sum of them.

In practice, the worst offender is often “start everything only after the user clicks talk.” If the avatar session has to be created, connected, and negotiated at that moment, you are paying the full setup cost on the critical path.

What “warming a session” actually means

Warming a session means doing the expensive or latency-sensitive work before the user needs the avatar to speak. The goal is to move setup off the critical path while keeping the session in a state where it can produce media quickly when needed.

That can include:

• creating the realtime avatar session early

• keeping the transport and media pipeline alive

• joining the session before the first utterance

• preloading a specific avatar configuration, voice, or instructions

• keeping the browser-side embed or client connection ready but hidden until needed

The exact implementation depends on your app surface, but the principle is always the same: create slack before the user feels the latency.

Designing for lower first-frame latency

1) Create the session before the user needs it

The simplest and most effective optimization is to initialize the avatar session at page load, agent startup, or shortly after you can reasonably predict the user will need it.

For example, in a voice agent you might create the avatar session when the call connects, not when the agent first speaks. In a web app, you might start warming after the page becomes interactive, or after the user opens a chat panel. The session can sit idle for a short period, ready to render the first frame immediately when the agent begins speaking.

The trade-off is straightforward: you spend some resources earlier, and in some cases you may create sessions that never get used. That is usually worth it when the perceived latency matters.

2) Separate session readiness from visible presentation

A common mistake is to tie “session exists” to “avatar is shown.” Those are different states.

For example, an embed can be connected and ready while still hidden behind your UI. A voice agent can have its avatar session initialized while the caller is still listening to the assistant’s greeting. The UI can reveal the avatar only when the first audio or video frame is already on its way.

This matters because many first-frame delays are caused by waiting for the session to be created after the user has already asked for a response. If you decouple readiness from visibility, the user sees the avatar appear much faster.

3) Warm the exact configuration you will use

Warming only helps if you warm the same shape of session that you will actually serve. That means the right avatar, the right voice, and the right runtime configuration. If your production path switches between several avatars or instructions, warming the wrong one is just adding another cold start later.

In other words: prefer “precreate the specific session instance that will be used next” over “create some generic session and reconfigure it at the last second.” The latter often just moves the delay around.

4) Measure the right latency

When people say “first-frame delay,” they often mix at least three metrics:

• time to session ready

• time to first video frame

• time to first audible output or lip-synced speech

If you’re debugging, measure them separately. The session may be ready quickly while the client still waits on signaling. Or the connection may be established quickly while the first generated speech is delayed upstream. Treating those as one number makes optimization guessy.

At minimum, log timestamps for session creation, media-ready, and first rendered frame. That will tell you whether warming is actually helping or whether the bottleneck moved somewhere else.

Implementation patterns that work

There are a few patterns I see repeatedly in production systems.

Pattern A: warm on page load, show on demand

This is the right default for website embeds and interactive demos. Start the session as soon as the page is stable, but keep the avatar hidden until the user starts a conversation or presses a button.

That reduces first-frame delay without making the UI feel eager. It also gives you time for the browser to complete any network setup before the user asks for output.

Pattern B: warm when the agent enters an “active” state

For voice agents, “active” usually means the user has joined the call or the bot has been placed into a conversation. Warming at that point is a good compromise: the session exists before the first utterance, but you are not paying to keep it warm forever.

This is especially useful in systems where the assistant may remain silent for a few seconds while the caller is listening to an intro or navigating a menu. You can finish avatar setup during that silence.

Pattern C: pre-warm from upstream signals

If your application has a reliable prediction of imminent use, use it. Examples:

• a user opens a support widget

• a call is accepted

• a game NPC is about to enter a dialogue state

• a sales agent room is created and a participant is assigned

The closer your signal is to actual intent, the more valuable the warmup becomes. Just be careful not to trigger warm sessions on noisy or low-confidence signals, or you’ll spend resources for little latency gain.

One practical way to do it with Protoface

Protoface supports this kind of workflow through its session-oriented API surface, which is the part that matters here. You can create and manage avatars and realtime sessions over the REST API, or drive the same flow from the Python SDK in backend code. If you’re embedding into a LiveKit agent, the plugin path is also suitable for early initialization because the avatar can be attached as part of agent startup rather than later in the conversation.

A minimal backend warmup flow looks like this:

import requests

print(session)

import requests

print(session)

import requests

print(session)

That example is intentionally schematic: the exact request shape is documented in the API docs, but the important part is that session creation happens before the user is waiting on it.

If you’re using LiveKit Agents, the same idea applies conceptually: initialize the avatar attachment early in the agent lifecycle so the first spoken response does not also pay the avatar setup cost. The plugin and examples in the repo are the best place to start if that’s your stack: https://github.com/protoface-ai/protoface-quickstart-openai-realtime.

WebRTC and browser-specific gotchas

Warming helps, but it does not eliminate the realities of realtime transport. If the avatar is delivered over WebRTC, the browser still has to complete signaling and connectivity checks before media can flow. That means:

• the warm session should exist before the user expects output

• the client should avoid waiting for the first assistant token before starting transport setup

• you should expect some variability across networks, especially on mobile or restrictive corporate environments

Another subtle issue is idle timeout. A warmed session that sits too long may be cleaned up by the service or network intermediaries. If your app has long pre-roll periods, you may need to refresh the warm session rather than assuming it stays ready indefinitely.

Also remember that “warming” should not mean “keep every session open forever.” That is usually the wrong economic trade-off. It is better to warm narrowly and intentionally than to accumulate a lot of half-idle realtime infrastructure.

How to decide whether warming is worth it

You should warm sessions in advance if one or more of these are true:

• the avatar must appear immediately after the user takes action

• you run into noticeable cold starts on first utterance

• your product depends on a polished conversational feel

• you can predict demand a few seconds ahead of time

You may not need it if the avatar is mostly decorative, if latency is already hidden behind other work, or if session churn is too high to justify precreation. As with most realtime systems, the right answer is the one that gives you the best user-perceived latency at an acceptable cost.

Conclusion

Reducing first-frame delay is mostly about moving session setup out of the user’s critical path. Warm the session before it is needed, keep readiness separate from visibility, and measure session-ready time and first-frame time independently. Those changes usually buy you more than micro-optimizing downstream rendering.

If you’re implementing this with a Protoface integration, start with the docs at https://docs.protoface.com, then wire the warmup into the surface that best matches your app: REST for backend orchestration, Python for programmatic control, or the LiveKit plugin when the avatar lives inside a voice agent. The quickest path to a better UX is usually not a new model—it’s better timing.