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
JevCoreML
GodModeAI2025
Provides Core ML decision models, a Swift package, an HTTP server, and a demo app
laya
xosi
Runs Laya typed-decision inference locally on Apple Silicon with MLX
laya-mlx-ddz
smile-magic
A browser-based 斗地主 game lets two local AI seats play using Laya on Apple Silicon
decide
gopaljigaur
Provides a Python client that chains Laya with hosted and other decision-model backends
jev-laya-benchmark
EnesDemir143
Benchmarks TypeSafe Jev and Laya-MLX on structured issue classification
laya-learning-demo
Daryl9441
A runnable tutorial teaches Laya decision primitives, routing, fine-tuning, and comparison with Jev
tetris_for_laya
fchange
Provides a terminal Tetris environment where a local Laya model chooses each action
laya-mlx-per-turn-classifier
M37Labs
Demonstrates per-turn customer message classification with Laya on Apple Silicon
laya-rust
zerodegress
Implements a Rust inference engine for Laya with CUDA, CPU, and MLX backends
laya-exp
changtimwu
Experiments comparing an MLX Laya port with Jev and a TypeSafe-compatible Laya client
laya-lab
Alevsk
Runs Laya locally with reproducible scenarios that explore its capabilities on Apple Silicon
Tern
AmRitJain0442
A model router uses local Laya-MLX inference to select between configurable provider tiers
lime-jev
yzxoi
Adds optional Laya reranking to a local Chinese pinyin input method on Apple Silicon
laya-decision-api
bmw8080
An HTTP service wraps local Laya with OpenAPI documentation and Java, TypeScript, and Python SDKs
laya-vs-dijkstra
antonellof
Compares Laya MLX pathfinding decisions with Dijkstra on seeded weighted mazes
code-oracle
wahyuzero
Verifies code changes with AST checks and Laya-based residual-drift evaluation
laya-browser
Benny93
Uses a local Laya model to resolve plain-English selectors in browser automation
ghosthand
ameerhmz
A local macOS voice and computer-use agent uses Laya for typed decisions
pi-laya-skill-router
ericmjl
Routes pi coding-agent requests to relevant skills using a local Laya model
laya-local-guard
Kamandlou
Classifies content for safety risks locally with Laya-MLX and detects PII and secrets
laya-system1-snake
Kamandlou
A terminal Snake game uses Laya MLX to choose moves from structured game-state features
docweave
pareshbhangale
Converts PDFs to Markdown and uses Jev or Laya alongside local models for layout routing and table auditing
laya-agent
inboxxobni
Runs Laya typed decisions locally on Apple Silicon and provides an HTTP server and agent integrations
laya-router
vuongvm812
Sets up local Laya classification and Jev Router proxies for coding clients
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