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Local LLM inference on macOS using a Swift MLX host process + a TypeScript client / AI SDK provider.
This README is shown on the npm package page. The repo contains additional development notes.
Requirements
darwin/arm64)Install
npm i mlx-ts
During install, mlx-ts downloads a prebuilt mlx-host (Swift) binary + mlx.metallib from GitHub Releases (no Xcode required).
Use with the AI SDK
import { createMlxProvider } from "mlx-ts";
import { generateText, streamText } from "ai";
const modelId = "mlx-community/Llama-3.2-1B-Instruct-4bit";
const mlx = createMlxProvider({
model: modelId,
// optional:
// modelsDir: "/path/to/your/models-cache",
// hostPath: process.env.MLX_HOST_BIN,
});
const model = mlx.languageModel(modelId);
// stream
const s = await streamText({
model,
maxTokens: 64,
messages: [{ role: "user", content: "Say hello from a local MLX model." }],
});
for await (const chunk of s.textStream) process.stdout.write(chunk);
process.stdout.write("\n");
// one-shot
const g = await generateText({
model,
maxTokens: 64,
messages: [{ role: "user", content: "Summarize MLX in one sentence." }],
});
console.log(g.text);
MLX_HOST_DEVICE=cpu (default is gpu).MLX_HOST_BIN=/path/to/mlx-host or pass { hostPath } to createMlxProvider.~/Library/Caches/mlx-ts/models).{ modelsDir } to createMlxProvider or set MLX_MODELS_DIR.mlx-ts downloads assets from: set MLX_TS_HOST_BASE_URL (base URL containing mlx-host and mlx.metallib).OpenCode supports OpenAI-compatible providers and allows setting options.baseURL (OpenCode Providers) and selecting models via provider_id/model_id (OpenCode Models).
mlx-ts ships a small OpenAI-compatible local server:
# Start local server (choose any MLX model id)
npx mlx-ts-opencode --model mlx-community/Llama-3.2-1B-Instruct-4bit --port 3755
# Generate an opencode.json snippet
npx mlx-ts-opencode --print-config --model mlx-community/Llama-3.2-1B-Instruct-4bit --port 3755 > opencode.json
FAQs
AI SDK provider for local MLX (Swift) models on Apple Silicon (macOS).
The npm package mlx-ts receives a total of 9 weekly downloads. As such, mlx-ts popularity was classified as not popular.
We found that mlx-ts demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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