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omon

PyPI version Homebrew Python 3.10+ License: MIT stdlib only

Ollama tells you names. omon tells you what they mean.

GitHub · PyPI · Homebrew tap · Issues

Ollama's CLI shows model tags and file sizes. omon decodes those tags, benchmarks performance on your hardware, tracks what's eating your RAM, and tells you what to run — or remove.

omon demo

Why omon?

  • Decode cryptic model names35b-a3b-coding-nvfp4 becomes "Qwen3.5 · 35B (3B active) · Code generation · NVIDIA FP4 · vision, thinking, tools"
  • Benchmark on your machine — cold/warm load times, tok/s, memory footprint; compare models side-by-side
  • Know what to run — hardware-aware suggestions, successor alerts, cleanup recommendations for stale models
  • Local-first, zero dependencies — Python stdlib only. No venv conflicts, no supply chain risk, no cloud calls

Screenshots

Click an image for full size.

Status overview (omon)

omon status overview — click for full size

Live TUI (omon watch)

omon live TUI — click for full size

Web dashboard (omon serve)

omon web dashboard — click for full size

Install

Pick one — you only need a single install path.

PyPI (macOS, Linux; recommended):

pipx install omon
# or: pip install omon

Homebrew (macOS):

brew tap LightbridgeLab/omon
brew install omon

From source:

pipx install git+https://github.com/LightbridgeLab/OllamaMon.git

Requires Python 3.10+ and a running Ollama instance.

Upgrade: pipx upgrade omon or brew upgrade omon

Quick start

omon                          # status overview: server, models, RAM, pressure
omon list                     # decoded model inventory
omon bench llama3.2:3b        # benchmark load time and tok/s
omon watch                    # live TUI (press q to quit)

More commands: omon hw, omon suggest --task coding, omon cleanup, omon serve.

Developing from source

git clone https://github.com/LightbridgeLab/OllamaMon.git && cd OllamaMon
make install
make dev              # common local test commands
make check            # run tests

See AGENTS.md for architecture rules and the pipx/Homebrew vs .venv note.

Developing from source

git clone https://github.com/LightbridgeLab/OllamaMon.git && cd OllamaMon
make install
make dev              # common local test commands
make check            # run tests

See AGENTS.md for architecture rules and the pipx/Homebrew vs .venv note.

Documentation

Requirements

  • Python 3.10+
  • Ollama running locally (or --host for remote)
  • macOS or Linux (Apple Silicon is the primary target)

License

MIT — see LICENSE.

About

Local-first CLI for Ollama: show resource usage, decode model tags, benchmark tok/s, monitor RAM, and suggest what to run. Python stdlib only.

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