jundot/omlx

Every LLM server I tried made me choose between convenience and control. I wanted to pin everyday models in memory, auto-swap heavier ones on demand, set context limits - and manage it all from a menu bar.

oMLX persists KV cache across a hot in-memory tier and cold SSD tier - even when context changes mid-conversation, all past context stays cached and reusable across requests, making local LLMs practical for real coding work with tools like Claude Code. That's why I built it.

Install

macOS App

Download the .dmg from Releases, drag to Applications, done. The app includes in-app auto-update, so future upgrades are just one click.

Homebrew

brew tap jundot/omlx https://github.com/jundot/omlx
brew install omlx

# Upgrade to the latest version
brew update && brew upgrade omlx

# Run as a background service (auto-restarts on crash)
brew services start omlx

# Optional: MCP (Model Context Protocol) support
/opt/homebrew/opt/omlx/libexec/bin/pip install mcp

From Source

git clone https://github.com/jundot/omlx.git
cd omlx
pip install -e .          # Core only
pip install -e ".[mcp]"   # With MCP (Model Context Protocol) support

Requires macOS 15.0+ (Sequoia), Python 3.10+, and Apple Silicon (M1/M2/M3/M4).

Quickstart

macOS App

Launch oMLX from your Applications folder. The Welcome screen guides you through three steps - model directory, server start, and first model download. That's it. To connect OpenClaw, OpenCode, or Codex, see Integrations.

CLI

omlx serve --model-dir ~/models

The server discovers LLMs, VLMs, embedding models, and rerankers from subdirectories automatically. Any OpenAI-compatible client can connect to http://localhost:8000/v1. A built-in chat UI is also available at http://localhost:8000/admin/chat.

Homebrew Service

If you installed via Homebrew, you can run oMLX as a managed background service:

brew services start omlx    # Start (auto-restarts on crash)
brew services stop omlx     # Stop
brew services restart omlx  # Restart
brew services info omlx     # Check status

The service runs omlx serve with zero-config defaults (~/.omlx/models, port 8000). To customize, either set environment variables (OMLX_MODEL_DIR, OMLX_PORT, etc.) or run omlx serve --model-dir /your/path once to persist settings to ~/.omlx/settings.json.

Logs are written to two locations:

• Service log: $(brew --prefix)/var/log/omlx.log (stdout/stderr)
• Server log: ~/.omlx/logs/server.log (structured application log)

Features

Supports text LLMs, vision-language models (VLM), OCR models, embeddings, and rerankers on Apple Silicon.

Admin Dashboard

Web UI at /admin for real-time monitoring, model management, chat, benchmark, and per-model settings. Supports English, Korean, Japanese, and Chinese. All CDN dependencies are vendored for fully offline operation.

Vision-Language Models

Run VLMs with the same continuous batching and tiered KV cache stack as text LLMs. Supports multi-image chat, base64/URL/file image inputs, and tool calling with vision context. OCR models (DeepSeek-OCR, DOTS-OCR, GLM-OCR) are auto-detected with optimized prompts.

Tiered KV Cache (Hot + Cold)

Block-based KV cache management inspired by vLLM, with prefix sharing and Copy-on-Write. The cache operates across two tiers:

• Hot tier (RAM): Frequently accessed blocks stay in memory for fast access.
• Cold tier (SSD): When the hot cache fills up, blocks are offloaded to SSD in safetensors format. On the next request with a matching prefix, they're restored from disk instead of recomputed from scratch - even after a server restart.

Continuous Batching

Handles concurrent requests through mlx-lm's BatchGenerator. Prefill and completion batch sizes are configurable.

Claude Code Optimization

Context scaling support for running smaller context models with Claude Code. Scales reported token counts so that auto-compact triggers at the right timing, and SSE keep-alive prevents read timeouts during long prefill.

Multi-Model Serving

Load LLMs, VLMs, embedding models, and rerankers within the same server. Models are managed through a combination of automatic and manual controls:

• LRU eviction: Least-recently-used models are evicted automatically when memory runs low.
• Manual load/unload: Interactive status badges in the admin panel let you load or unload models on demand.
• Model pinning: Pin frequently used models to keep them always loaded.
• Per-model TTL: Set an idle timeout per model to auto-unload after a period of inactivity.
• Process memory enforcement: Total memory limit (default: system RAM - 8GB) prevents system-wide OOM.

Per-Model Settings

Configure sampling parameters, chat template kwargs, TTL, model alias, model type override, and more per model directly from the admin panel. Changes apply immediately without server restart.

• Model alias: set a custom API-visible name. /v1/models returns the alias, and requests accept both the alias and directory name.
• Model type override: manually set a model as LLM or VLM regardless of auto-detection.

Built-in Chat

Chat directly with any loaded model from the admin panel. Supports conversation history, model switching, dark mode, reasoning model output, and image upload for VLM/OCR models.

Model Downloader

Search and download MLX models from HuggingFace directly in the admin dashboard. Browse model cards, check file sizes, and download with one click.

Integrations

Set up OpenClaw, OpenCode, and Codex directly from the admin dashboard with a single click. No manual config editing required.

Performance Benchmark

One-click benchmarking from the admin panel. Measures prefill (PP) and text generation (TG) tokens per second, with partial prefix cache hit testing for realistic performance numbers.

macOS Menubar App

Native PyObjC menubar app (not Electron). Start, stop, and monitor the server without opening a terminal. Includes persistent serving stats (survives restarts), auto-restart on crash, and in-app auto-update.

API Compatibility

Drop-in replacement for OpenAI and Anthropic APIs. Supports streaming usage stats (stream_options.include_usage), Anthropic adaptive thinking, and vision inputs (base64, URL).

Tool Calling & Structured Output

Supports all function calling formats available in mlx-lm, JSON schema validation, and MCP tool integration. Tool calling requires the model's chat template to support the tools parameter. The following model families are auto-detected via mlx-lm's built-in tool parsers:

Models not listed above may still work if their chat template accepts tools and their output uses a recognized <tool_call> XML format. For tool-enabled streaming, assistant text is emitted incrementally while known tool-call control markup is suppressed from visible content; structured tool calls are emitted after parsing the completed turn.

Models

Point --model-dir at a directory containing MLX-format model subdirectories. Two-level organization folders (e.g., mlx-community/model-name/) are also supported.

~/models/
├── Step-3.5-Flash-8bit/
├── Qwen3-Coder-Next-8bit/
├── gpt-oss-120b-MXFP4-Q8/
├── Qwen3.5-122B-A10B-4bit/
└── bge-m3/

Models are auto-detected by type. You can also download models directly from the admin dashboard.

CLI Configuration

# Memory limit for loaded models
omlx serve --model-dir ~/models --max-model-memory 32GB

# Process-level memory limit (default: auto = RAM - 8GB)
omlx serve --model-dir ~/models --max-process-memory 80%

# Enable SSD cache for KV blocks
omlx serve --model-dir ~/models --paged-ssd-cache-dir ~/.omlx/cache

# Set in-memory hot cache size
omlx serve --model-dir ~/models --hot-cache-max-size 20%

# Adjust batch sizes
omlx serve --model-dir ~/models --prefill-batch-size 8 --completion-batch-size 32

# With MCP tools
omlx serve --model-dir ~/models --mcp-config mcp.json

# HuggingFace mirror endpoint (for restricted regions)
omlx serve --model-dir ~/models --hf-endpoint https://hf-mirror.com

# API key authentication
omlx serve --model-dir ~/models --api-key your-secret-key
# Localhost-only: skip verification via admin panel global settings

All settings can also be configured from the web admin panel at /admin. Settings are persisted to ~/.omlx/settings.json, and CLI flags take precedence.

FastAPI Server (OpenAI / Anthropic API)
    │
    ├── EnginePool (multi-model, LRU eviction, TTL, manual load/unload)
    │   ├── BatchedEngine (LLMs, continuous batching)
    │   ├── VLMEngine (vision-language models)
    │   ├── EmbeddingEngine
    │   └── RerankerEngine
    │
    ├── ProcessMemoryEnforcer (total memory limit, TTL checks)
    │
    ├── Scheduler (FCFS, configurable batch sizes)
    │   └── mlx-lm BatchGenerator
    │
    └── Cache Stack
        ├── PagedCacheManager (GPU, block-based, CoW, prefix sharing)
        ├── Hot Cache (in-memory tier, write-back)
        └── PagedSSDCacheManager (SSD cold tier, safetensors format)

Development

CLI Server

git clone https://github.com/jundot/omlx.git
cd omlx
pip install -e ".[dev]"
pytest -m "not slow"

macOS App

Requires Python 3.11+ and venvstacks (pip install venvstacks).

cd packaging

# Full build (venvstacks + app bundle + DMG)
python build.py

# Skip venvstacks (code changes only)
python build.py --skip-venv

# DMG only
python build.py --dmg-only

See packaging/README.md for details on the app bundle structure and layer configuration.

Contributing

Contributions are welcome! See Contributing Guide for details.

• Bug fixes and improvements
• Performance optimizations
• Documentation improvements

License

Apache 2.0

Acknowledgments

• MLX and mlx-lm by Apple
• mlx-vlm - Vision-language model inference on Apple Silicon
• vllm-mlx - oMLX started from vllm-mlx v0.1.0 and evolved significantly with multi-model serving, tiered KV caching, VLM with full paged cache support, an admin panel, and a macOS menu bar app
• venvstacks - Portable Python environment layering for the macOS app bundle
• mlx-embeddings - Embedding model support for Apple Silicon
• llm-compressor - Reference AWQ implementation for MoE models, used as design reference for oQ weight equalization