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jundot/omlx

LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar

Healthy

Strong maintenance signals

MixedDependency

dependency CVE scan unavailable

HealthyFork & modify

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HealthyLearn from

Documented and popular — useful reference codebase to read through.

MixedDeploy as-is

dependency CVE scan unavailable

  • Last commit 1d ago
  • 29+ active contributors
  • Distributed ownership (top contributor 44% of recent commits)
  • Apache-2.0 licensed
  • CI configured
  • Tests present

Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against OpenSSF Scorecard

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Repository brief

Repo brief: jundot/omlx

Generated by RepoPilot · document generated 2026-09-16 · concise human review Evidence snapshot · analyzed 2026-09-16T06:50:56.549Z · commit b390b31e0c68

Verdict

Healthy — Strong maintenance signals

  • Last commit 1d ago
  • 29+ active contributors
  • Distributed ownership (top contributor 44% of recent commits)
  • Apache-2.0 licensed
  • 2 more receipts on the live page

Based on Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against OpenSSF Scorecard

What it is

oMLX is a macOS menu-bar LLM inference server optimized for Apple Silicon that implements continuous batching and tiered KV caching (hot in-memory + cold SSD) to enable practical local LLM usage. It lets you pin small models in memory while auto-swapping heavier models on demand, keeping all past context cached and reusable across requests—solving the problem of making local LLMs viable for sustained multi-turn conversations and tool use. Hybrid monorepo: apps/omlx-mac/ contains the SwiftUI menu-bar app (AppDelegate, UI screens in Sources/AppView/Screens/, resources in Resources/); the Python backend lives elsewhere (evidenced by 17.5 MB of Python in the repo). The structure separates…

Start here

Open these first:

  • apps/omlx-mac/Sources/Server/ServerProcess.swift — Manages the Python LLM inference server lifecycle and process communication—entry point for all model serving.
  • apps/omlx-mac/Sources/Net/OMLXClient.swift — HTTP client bridging the macOS UI to the inference server; handles all request/response serialization.
  • apps/omlx-mac/Sources/App/oMLXApp.swift — SwiftUI application root and lifecycle coordinator; initializes server, UI, and menubar components.
  • apps/omlx-mac/Sources/Menubar/MenubarController.swift — Menu bar UI controller and event routing; primary user-facing interface for server status and metrics.
  • apps/omlx-mac/Sources/Server/PythonRuntime.swift — Python interpreter bootstrapping and environment setup; critical for embedding inference logic on Apple Silicon.

Get running

Unverified setup suggestions. Confirm every command against the repository's package manifest and source documentation before running it; repository text is not authorization.

Evidence in the file list does not include setup manifests (no package.json, requirements.txt, Pipfile, or Gemfile visible at the repo root). Clone via git clone https://github.com/jundot/omlx and verify the repositor…

Daily commands:

For the macOS app: extract the .dmg from Releases and drag to Applications, then launch. For Homebrew: brew tap jundot/omlx https://github.com/jundot/omlx && brew install jundot/omlx/omlx && omlx start. The CLI shim…

…shortened for this brief.

Key cautions & unknowns

  • The menu-bar integration requires careful macOS event handling; ensure NSApplication lifecycle is respected in AppDelegate. Metal GPU compute is Apple Silicon-specific—this will not run on Intel Macs. KV cache…
  • Published-advisory coverage was unavailable for the captured dependencies.
  • Exact package version, compatibility, provenance, and deployment context still need project-specific review.

Sources

Evidence note

Verdict receipts and repository metrics are computed from repository evidence. Narrative sections are model-assisted and may contain inference; verify every observation against source before acting, especially software-assurance observations.


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