Integrating LLM with Augmented Reality: A Step-by-Step Guide

Augmented reality applications are moving beyond static overlays toward intelligent, context-aware companions. By pairing AR glasses or mobile headsets with large language models, developers can build experiences that interpret the physical world, answer questions about nearby objects, and maintain multi-turn conversations across a user's entire session. The challenge is not only latency and vision understanding, but also managing the growing context of an AR session without costs scaling unpredictably. Oxlo.ai provides an inference platform built for this: request-based pricing, multimodal models, and full OpenAI SDK compatibility so you can plug it into an existing AR pipeline without rewriting your client code.

Architecture Overview

An AR-LLM integration usually follows a three-layer pattern. The capture layer runs on the device, streaming camera frames, depth data, and user speech to a lightweight client. The reasoning layer receives this multimodal input, maintains session memory, and decides what the user is asking or what action to take. The rendering layer draws labels, bounding boxes, or avatar responses back into the user's field of view.

Oxlo.ai sits in the reasoning layer. Because its API is fully OpenAI SDK compatible, you can point your existing Python, Node.js, or Unity HTTP client to https://api.oxlo.ai/v1 and start sending chat completions, vision requests, or tool calls immediately. There are no cold starts on popular models, which means the first request after a period of inactivity returns as quickly as a warm one, a critical behavior for AR users who expect instant reactions when they glance at an object.

Choosing the Right Model for AR Workloads

AR workloads mix vision, spatial reasoning, and extended dialogue. Oxlo.ai offers several models that map directly to these needs.

For scene understanding and visual question answering, Kimi VL A3B accepts image inputs and can describe environments, read text on signs, or identify objects from a camera feed. If your application runs agents that need to plan multi-step tasks or call tools, Qwen 3 32B and Kimi K2.6 support advanced reasoning and agentic coding. For sessions where the user roams a warehouse or campus and you must retain prior context across hundreds of turns, DeepSeek V4 Flash offers a one-million-token context window, and Kimi K2.6 provides 131K tokens. General-purpose dialogue and instruction following are well served by Llama 3.3 70B.

Because Oxlo.ai charges per request rather than per token, a long-context AR session does not become exponentially more expensive as the conversation history grows. For agentic workloads that iterate over large system prompts or prior turns, this can be significantly cheaper than token-based alternatives. You can verify current plan details at https://oxlo.ai/pricing.

Building the AR Client and Capturing Context

Most AR frameworks, such as ARKit, ARCore, or WebXR, expose camera frames as base64-encoded JPEGs or raw byte arrays. Your backend receives these frames, bundles them with the user's text query, and forwards them to the LLM.

Below is a minimal Python backend using FastAPI. It accepts an image and a user prompt, then calls Oxlo.ai with the OpenAI SDK. The example uses Kimi VL A3B for vision understanding.

import base64
from fastapi import FastAPI, File, UploadFile, Form
from openai import OpenAI

app = FastAPI()
client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"  # Replace with your key
)

@app.post("/describe")
async def describe_scene(
    image: UploadFile = File(...),
    prompt: str = Form("What do you see in this scene?")
):
    contents = await image.read()
    b64 = base64.b64encode(contents).decode("utf-8")

    response = client.chat.completions.create(
        model="kimi-vl-a3b",
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{b64}"
                        }
                    }
                ]
            }
        ],
        max_tokens=512
    )
    return {"description": response.choices[0].message.content}

This pattern keeps the heavy inference off the device. The AR headset only needs to capture frames and render the returned text or commands.

Streaming and Tool Use for Real-Time Overlays

AR users do not wait well. Blocking for several seconds while the model finishes an entire paragraph breaks immersion. Oxlo.ai supports streaming responses, so your client can start rendering words or parsing JSON deltas as soon as the first tokens arrive.

Many AR applications also need to ground LLM outputs in 3D space. You can define tools that the model calls to place anchors, highlight objects