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
wee-hours
tianhaoz95
Logs newborn care events and uses an on-device Laya model to interpret voice inputs
laya-browser-agent
Sreenuraj
Automates web tasks locally using Laya MLX and Playwright tools
Quantlabs_G-L
rmrodriguezgit
Simulates Polymarket BTC up/down trading using GANs, Q-learning, and Laya MLX audits
laya-local
ira-at-work
Benchmarks local Laya for coding-agent command gating and context-compaction decisions
laya-pokesurvivor
garykhfung
Uses a local Laya checkpoint to control a browser-based PokéSurvivor game on Apple Silicon
laya-api
snorripall
Provides an API for running the Laya model on Nvidia and Apple Silicon hardware
laya-mobilecli
nichotined
Resolves plain-English mobile UI tap targets with the Laya typed-decision model
flylaya
leewbl
A fruit-fly behavior study combines connectome-driven motion with a Laya intent layer
laya-lab
karth2512
Benchmarks Laya classification and fine-tuning methods against classical baselines and the Jev API
sys1_mcp
dmonteroh
Provides an MCP server and HTTP daemon for System One decision models, including an in-process Laya backend
laya-decision-lab
geomichelon
Runs local Laya inference for ticket triage with typed decisions, policy review, and sample evaluation
pi-laya-mlx
a3harva
Screens coding-agent tool calls with a fine-tuned Laya model running locally on Apple Silicon
laya-docker
thomashaertel
Runs Laya on AMD GPUs in Docker and exposes Jev-compatible decision APIs and an MCP server
laya-fastapi
builtbybasit
Provides a local Jev-compatible API powered by Laya with MLX and PyTorch backends
laya-oneshot
gergeisabo
Runs one typed decision with Laya on demand, then exits without keeping a server running
trend-agent-webapp
Lucas-0911
Scores media trends with Jev or local Laya-MLX and feeds the results into an analysis workflow
pmax
will702
A Go Mac productivity CLI optionally uses an MLX/Laya sidecar for semantic search and improvement
jev-vs-laya
KPMGE
Compares Laya base and fine-tuned models with hosted Jev for support-message triage
laya-zcode-stack
Ydjin1984
Runs a local Laya decision engine and ZCode plugin with MCP tools for coding-agent workflows
laya-browser-grounding
Who-Code
A study and demo using Laya-MLX to ground browser automation actions on page elements
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