omon 0.5.0

A simple, local-first monitoring and management tool for Ollama

omon

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

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.

Why omon?

• Decode cryptic model names — 35b-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

Install

pipx install omon

Or from source:

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

Requires Python 3.10+ and a running Ollama instance.

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.

Documentation

• Command reference — full docs for every command, config, and completions
• Design history — how and why omon was built
• Roadmap — what's planned next
• Security — network exposure and data storage
• Contributing — architecture rules and conventions

Requirements

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

License

MIT — see LICENSE.