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-adblock
Samuel-Ku
Uses a local MLX Laya model to identify and remove likely ads from web pages
laya.mbt
majikxu
Implements Laya typed-decision inference in MoonBit with CPU and Metal backends
Laya_TESTSUITE
sirwari
Provides Laya tests, demos, routing examples, and MLX setup guidance
laya-todo
firede
Classifies to-do items locally with Laya and compares its predictions with an optional Kev backend
laya-mlx-rs
andyjusa
A native Rust and MLX implementation runs Laya decision models on Apple Silicon
laya-doom-agent
Jaiswal-Siddhant
Uses Laya-MLX to choose actions for an autonomous DOOM agent
laya-mlx-advisor
esse
Uses a resident Laya-MLX model to adapt reasoning effort in Codex CLI and Claude Code
obsidian-laya-tagger
tyPhoon-collab
Automatically tags Obsidian notes using the local Laya MLX typed-decision model
laya-mcp-server
ocha-no-taiko
Wraps the laya-mlx typed-decision model in an MCP server for tools such as Claude Code
laya-flappy-bird
GadhiyaRaj
A Flappy Bird-style game uses Laya to choose real-time flap or coast actions
laya-claude-code
harshadptl
Claude Code plugin that uses laya-mlx to pick the model tier and time context compaction
laya-or-jev-pong
bananadonn
Compares local Laya and hosted Jev decision APIs by having them control paddles in Pong
laya-mlx-jev-compat
matt-starburst
Serves a local MLX Laya model through a Jev-compatible HTTP endpoint
e2e-ui-test-laya
spencerlepine
A local browser-use agent uses Laya through MLX to choose actions and verify task completion
system-one
arhamj
Serves Laya typed decisions from Apple silicon over a private Tailscale endpoint
ai-update-radar-lab
starhunt
Tests Laya-based project relevance decisions and compares results with saved Jev evaluations
Jev
Caho1
A research repository documents Laya fine-tuning, evaluation, local inference and browser automation experiments
Tuantuan
JunbiaoXue
Provides a native macOS desktop companion that uses local Laya decisions to choose behavior
jev-like-model-playground
LiuWei997
A notebook template runs lightweight System 1 models, including Laya, locally on Apple Silicon with MLX
faceid-bench
mazenDDr
A face-unlock benchmark measures detection and recognition pipelines and tests Laya for yes/no decisions
laya
StevenJPx2
Provides a Swift and Core ML runtime, daemon, CLI, and distillation tools for Laya
LayaBasedAssistant
userasg
Uses Laya to route requests and gate risky actions in a local voice-and-text assistant
Laya.jl
AtelierArith
Runs Laya typed-decision models in Julia on CPU or Apple GPU
homebrew-tap
madeye
Provides Homebrew formulae, including one for OpenZL-Laya local integer routing
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.
More ways to use Laya