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-mlx
Gates-456
Runs open-weight Laya typed decisions locally on Apple Silicon using MLX
laya-doom
dylanbstorey
Runs Laya in a real-time control loop to play Doom on Apple Silicon
laya-js
johnhenry
Runs Laya typed decisions in JavaScript across MLX, WebGPU and CPU backends
laya-mlx-mcp
Blackphi6
Exposes Laya-MLX typed decisions through an MCP server for Apple Silicon
codex-laya-mcp
AetherZZ2
Exposes local Laya typed-decision tools to Codex through MCP on Apple Silicon
jev-laya-explore
NachiketKandari
Evaluates the local Laya decision model alongside TypeSafe's hosted Jev
laya-decision-web
LeeeeeeM
Runs a local browser demo for Laya decisions in Snake and side-scrolling game scenarios
laya-mlx-benchmarks
jayluxferro
Benchmarks Laya typed-decision models running through the MLX inference port
cam-laya-mcp
vdqvinh2004
Adds optional local Laya-MLX decision tools for coding agents
laya-snake-lab
shaunthebuilder
Combines a Snake game with Laya-powered autoplay, training, and live decision probabilities
laya-r-mlx
tjameswilliams
Provides a native Rust inference engine for Laya with MLX and candle backends
laya-mlx-windows-cuda
oboroge0
Documents running the MLX Laya model on Windows with an NVIDIA GPU
jev-systemone-local
katya4oyu
Plans a local Jev-compatible System One server with MLX, Core ML, and ONNX Laya backends
SEO-Agent-using-LAYA
07anishu12
Audits websites and uses Laya MLX inference to classify and prioritize SEO issues
MacJev-322M-4K-Laya
lawrence3699
Provides a fine-tuned Laya-derived decision model for local Mac agents
Autonomous-Decision-Intelligence-Platform-ADIP-
Ayyankhan101
Blueprints a local Laya-based decision platform using the laya-mlx runtime
MacJev-322M-4K-Laya-MLX
lawrence3699
Runs the MacJev typed-decision model natively on Apple silicon with MLX and compares it with Laya
stf-sim
JonRoosevelt
Simulates Brazilian Supreme Court cases using local Laya decisions, retrieval and bias probes
identity-jev
clawdreyhepburn
Fine-tunes Laya to rank identity and authentication standards from plain-language queries
zhiyan
liuqing0224
Provides macOS chat assistance with local Laya analysis and generated reply suggestions
Verdict
TobyNoSkillSon
Keeps local System One models, including Laya, ready for coding-agent classification and scoring
jev-context-rot
casperkwok
Reproduces an experiment comparing context sensitivity in Jev, Laya and Decider models
LayaService
wj1313677
Serves the Laya decision model through a local REST API using MLX on Apple Silicon
classifier-laya
josecruset
A local browser interface for testing Laya-MLX typed-decision models on Apple Silicon
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