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Run Laya on your Mac

Apple Silicon is the best-covered platform in the Laya ecosystem. Independent ports run the upstream checkpoints on the MLX GPU, through Core ML on the Neural Engine, or inside native Swift apps. Each port publishes its own speed and fidelity measurements, all taken on the author's own Mac.

308 projects

laya-typed-decisions-mlx

aac6fef

Runs Laya typed-decision inference natively on Apple silicon with MLX

MacJev-322M-4K-Laya-MLX

chaoliangUNSW

Runs the MacJev decision model natively on Apple silicon with an MLX runtime

MLXRuntimes & ports

laya-fast

DJLougen

Runs Laya typed decisions on Apple Silicon with MLX and optional Core ML execution

laya-multilingual-coreml

aac6fef

Exports multilingual Laya as a portable Core ML bundle for Apple devices

jev-tests

schacon

Compares Laya, Jev, Kev, and Claude in three macOS typed-decision demos

deqio

ILuce

Serves multiple typed-decision models, including Laya, through a local API and browser UI

jev-laya-benchmark

harrymunro

Benchmarks local MLX Laya against TypeSafe's hosted Jev on synthetic decision tasks

laya-mlx-voice-browser

aryanbhujade

Uses a local Laya-MLX model to turn spoken commands into browser actions on Mac

fast-browser

hamza-sahin

Automates Brave browser workflows through MCP using Jev or optional local Laya inference

Bud-Decision-Engine

BudEcosystem

Desktop app and local server for Laya and other Jev-like decision models

laya-mcp

wsargent

Runs local Laya inference on Apple Silicon and exposes typed-decision tools through an MCP server

structured-decision-bench

zhengbangbo

Benchmarks structured decisions from Qwen3, Jev, and Laya CoreML

laya-mcp

rdutra

An MCP server exposes local Laya-CoreML decisions through persistent decision tools

laya-router-skill

wangmiaozero

Adds local Laya decision routing to coding agents through skills and MCP

doomLaya

azalio

Trains and compares Laya and Jev agents playing FreeDoom with reproducible results

oh-my-laya

leo1394

Installs local Laya tools for classification, scoring, and routing in coding agents

laya-MLX-8bit

cavi-ai

An 8-bit MLX conversion of Laya checkpoints for Apple Silicon

MLXRuntimes & ports

localjev-mlx

rimusz

A self-hosted System One judgment service for Apple Silicon using laya-mlx

doom-war

rythmn1111

Compares local MLX Laya and hosted Jev as decision-makers in a Doom deathmatch

laya-jev-api

smallnest

Serves local Laya Core ML inference through a Jev-compatible HTTP API

openzl-laya

madeye

Adds optional on-device Laya routing to OpenZL compression on Linux and macOS

C / C++NVIDIA / CUDA

laya-search

giaphutran12

Searches YC companies using local Laya inference or the TypeSafe Jev API

laya-vs-jev

zaferayan

Benchmarks multilingual Laya against hosted Jev across 900 cases, three tasks, and six languages

mac-attack

dockndevai

A macOS app uses Laya decisions to direct harmless effects in a camera-based screensaver game

Pick a runtime

MLX (Python). laya-mlx is the most widely used port. It needs Apple Silicon, Python 3.11+ and macOS 14+:

pip install laya-mlx

import laya_mlx as laya

agent = laya.load("aac6fef/laya-mlx")
result = agent.predict(
    "I was billed twice. Please refund the duplicate.",
    {"department": {"type": "choice",
                    "instructions": "Who should handle this?",
                    "criteria": ["billing", "technical", "sales"]}},
)
print(result["answers"]["department"])

Its README reports 13.42 ms P50 for one short question with the 421M English checkpoint and 7.39 ms with the 322M multilingual one, FP16 on an M3 Max.

Core ML and the Neural Engine. laya-coreml needs macOS 15+ and Python 3.11 to 3.13 (pip install laya-coreml). Its README reports 4.98 ms P50 for one short multilingual decision on the M3 Max Neural Engine. laya-apple runs the MLX GPU and the Neural Engine together and serves a Jev-compatible API:

pip install 'laya-apple[serve,ane]'
laya-apple serve                                   # http://127.0.0.1:8642

Swift. laya-mlx-swift embeds Laya in a macOS app with MLX (about 804 MiB download). LayaKit is a Swift package over the laya-coreml bundles, and FluidUse ships a LayaManager for Core ML.

PyTorch MPS. The official laya package also runs on the Mac GPU; laya-mps wraps it in a local server with low-memory modes.

Caveats

All of these are independent ports, not official Convai Innovations releases. Most publish fidelity checks against upstream answers; read them.
The Neural Engine bundle in laya-coreml has a 96-token total limit covering question, options and state. Use the 1024-token general bundle for longer input.
Latency numbers are single-machine measurements. Your Mac, input length and question count will change them.