DVAIBridge.Desktop 4.0.1

DVAI-Bridge

The local OpenAI server you embed inside your app. One library. One HTTP wire. Every platform. Zero install for your users.

Docs: bridge.deepvoiceai.co

import { DVAI } from "@dvai-bridge/core";
import OpenAI from "openai";

const dvai = new DVAI({ backend: "transformers" });
await dvai.initialize();

const openai = new OpenAI({ baseURL: dvai.baseUrl, apiKey: "ignored" });
await openai.chat.completions.create({
  model: dvai.transformersModelId,
  messages: [{ role: "user", content: "Hello!" }],
});

That's it. A real OpenAI-compatible server is now running inside your app's own process. Point any OpenAI client — LangChain, the OpenAI SDK, the Vercel AI SDK, anything — at dvai.baseUrl and your agent code keeps working.

Built by Deep Voice AI.

Why it exists

Local AI works beautifully on a laptop with Ollama + LangChain. Then you try to ship the app and your users don't have Ollama. Mobile can't run it. Corporate IT won't add another daemon. So you reinvent the same plumbing — spawn an inference engine, bind a port, translate to OpenAI HTTP, handle CORS, manage lifecycle, wrap the accelerator of the day per platform — and do it all over again for every target OS.

DVAI-Bridge is that plumbing, packaged as a library, for every client platform.

What you get

• One OpenAI HTTP surface. Bound on 127.0.0.1 (or 0.0.0.0 for device-to-device). Streaming, embeddings, models, recovery — all built in.
• Six SDKs. @dvai-bridge/core + react + vanilla + capacitor, DVAIBridge (Swift / iOS), co.deepvoiceai:dvai-bridge (Kotlin / Android), @dvai-bridge/react-native, dvai_bridge (Flutter), co.deepvoiceai.dvai-bridge (.NET).
• Nine backends. WebLLM, Transformers.js, llama.cpp, Apple Foundation Models, MLX, CoreML / ANE, MediaPipe LLM, LiteRT, ONNX Runtime GenAI — selected per-platform, invisible to your agent code.
• Native acceleration wherever it runs: WebGPU in browsers, CUDA / Metal / Vulkan / DirectML on desktop, ANE / Metal / MLX on iOS, NNAPI / QNN Hexagon / GPU delegate on Android.
• Multimodal. Text, image, audio, video — declarative loader for cutting-edge models (Gemma 4, LLaVA, Idefics) without waiting for library updates.
• Distributed inference (v3.0+). Phone too slow? Offload to your laptop on the same Wi-Fi via mDNS pairing — same OpenAI wire, transparent to your code. Internet path via a self-hostable rendezvous server.
• DVAI Hub (v3.1+). A first-party desktop utility that turns any device into a strong-peer for the rest of your fleet. Brand-neutral install via Homebrew / winget / GitHub Releases, OR fork it for your own branded companion. Routes through Ollama / LM Studio / vLLM / llama-server / llamafile if you've already got those running.
• Zero user install. It's a library, not a daemon. npm install, cocoapods, gradle — your CI already has the muscle for it.

Supported platforms

Full quickstart per platform: bridge.deepvoiceai.co/guide/getting-started

Examples

// React
import { DVAIProvider, useDVAI } from "@dvai-bridge/react";
<DVAIProvider config={{ backend: "transformers" }}>
  <Chat />
</DVAIProvider>;
function Chat() {
  const { isReady, baseUrl } = useDVAI();
  return isReady ? <div>Local AI live at {baseUrl}</div> : <Loading />;
}

// iOS
let server = try await DVAIBridge.shared.start()
// server.baseUrl = "http://127.0.0.1:38883/v1"

// Android
val server = DVAIBridge.start(context)
// server.baseUrl = "http://127.0.0.1:38883/v1"

// Flutter
final state = await DVAIBridge.instance.start(
  backend: BackendKind.auto,
  modelPath: '/path/to/model.gguf',
);
// state.baseUrl = "http://127.0.0.1:38883/v1"

// .NET
var server = await DVAIBridge.Shared.StartAsync(new StartOptions {
    Backend = BackendKind.Auto,
    ModelPath = "/path/to/model.gguf",
});
// server.BaseUrl = "http://127.0.0.1:38883/v1"

Multimodal, streaming, embeddings, distributed offload, the Hub — everything's at the docs site.

What's new in v3.1

• DVAI Hub — Tauri desktop utility that's the strong-peer side of v3 distributed inference. brew install deepvoiceai/dvai-hub/dvai-hub (or winget install DeepVoiceAI.DVAIHub) → mobile apps on the same Wi-Fi pair with it and offload heavy inference. Guide →
• External-engine bridge. Hub surfaces Ollama / LM Studio / vLLM / llama-server / llamafile as additional backend pools so paired apps serve from whatever's already cached. Opt-in per engine.
• Strict substitution policy. Models with mismatched family / version / size / type are refused by default; quant-only mismatches gated behind a per-pairing preferBetterQuant flag. No silent mis-routing.
• HMAC-signed identity on /v1/chat/completions. Per-app audit logs surface who served what, with structured (appId, peerDeviceId, engine, requestedModel, servedModel, outcome) rows.
• Library finalization. httpBindHost (LAN bind), chatCompletionInterceptor (extension point), HMAC primitives re-exported, /v1/dvai/* routes actually dispatched, TransformersBackend Node-mode device fix. Migration v3.0 → v3.1 →

Robustness

Streaming-correct (SSE passthrough + blank-chunk detection), generation timeout, automatic engine-state recovery on fatal errors, port fallback, worker offloading, Private Network Access ready, CORS configured. The boring substrate so your agent code never has to think about it.

Licensing

Dual: free for development & personal use on localhost (verified at runtime). Commercial use requires a license key — info@deepvoiceai.co.

Contributing

PRs welcome.

pnpm install
pnpm build
bash scripts/build-all.sh   # full matrix (auto-skips per-host)

CONTRIBUTING.md for the PR flow. Per-platform contributor docs (iOS / Android / RN / Flutter / .NET) under docs/development/.

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