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
hekla-spjall
Hekla-dev
A Chrome side panel pairs on-device Gemma text generation with local Laya decisions
laya-mlx
HuSuuuu
Runs the Laya typed-decision model locally on Apple silicon using MLX
maclaya
jannisleifeld
Serves local Laya models on Apple Silicon through a Jev-compatible API with a dashboard and playground
laya-mlx-server
tankerwng2
Serves laya-mlx over HTTP using the Jev protocol and exposes an MCP interface
laya_vision-vs-SF2
beefkknd
Fine-tunes a Laya vision model to choose moves in Street Fighter II
LayaMail
Theani7
Prioritizes email locally by applying typed Laya decisions to messages
laya-lab
BramBouma
Offers two playable local simulations featuring real Laya decisions, rules baselines, and replay
laya-desktop
mategog
A desktop workbench runs Laya decisions offline, calibrates questions, and serves an API and MCP
laya-http-client
angelxmoreno
Provides a typed TypeScript client for Laya's HTTP protocol, with an MLX bridge sidecar
sayso
AshutoshKY
Uses on-device Laya to select Mac apps and system actions from voice commands
evidence-gate
AnotherMeatsack
A local workbench for evidence review, task routing, MCP tools, and reproducible Laya evaluations
verdict
iksnerd
Runs a fine-tuned Laya encoder locally to return typed judgments on Apple Silicon
laya-mlx-mirror-227
Shijiuwei
Runs Laya inference locally on Apple Silicon using MLX, with a terminal Snake demo
laya-plugin
Andrei-cloud
Provides an offline Laya decision plugin for coding agents through MCP and native integrations
laya-explore
Lewiscowles1986
Explores running Laya locally, including an MLX demo for Apple Silicon
laya-opencode-router
Wauuuuuu
A macOS OpenCode plugin routes tasks using local Laya classification and optional MLX models
laya_demo_test
j75689
A Snake demo compares Python and Rust Laya runners with ONNX Runtime and Core ML backends
app-intents-laya-study
junshanxu
Studies macOS App Intents and CLI agents with and without a local Laya next-action gate
hermes-laya-arbiter-integration
osviel91
Connects Hermes Agent to Laya/Arbiter decision tools through MCP
jev-clean
chengyixu
A macOS cleanup tool that uses Laya-MLX to investigate System Data and propose files for review
system-one-playground
goodboybeau
A macOS playground comparing Laya, Decider, Kev and Jev on accuracy, calibration and performance
layaUI-HK
whksoup
Runs a local Laya playground, games, benchmark, and agent skill
laya-eval
imaddde867
An evaluation compares local Laya with jevmlx and hosted Jev on public cases
laya-pro
mareknardella-lgtm
A hybrid AI platform combines local Laya decisions with generative models and workflow orchestration
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.
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