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Line to Rumi

Line to Rumi

A Roblox experience owned by L2XXY Games.

Social & hangoutENsocial simulationqueue mechanictrolling

Line to Rumi is a social-hangout experience created on 2025-08-31. The game has accumulated 1,519,496 lifetime visits and 263,279 favorites. It maintains an 82% like percentage. Currently, zero users are playing. The experience supports PC, mobile, tablet, and console platforms.

Description (written by the developer; not a statement by this register)

Wait your turn in line to Rumi! Cut your friends in line to be the first or troll everyone by causing a lockdown! ⭐- Favorite and leave a 👍like for more endings and updates! Game is supported on PC 🖥️, mobile 📱, tablet 📟, and console 🎮

Last updated by the developer 30 Oct 2025, 19:38 UTC.

0
playing (measured 10 Aug 2026, 14:21 UTC)
1,519,496
lifetime visits
82%
liked (1,617 👍 / 366 👎)
263,250
favourites (173.2 per 1k visits)
31 August 2025
created (per Roblox)

Play on Roblox →

Live players over time

Growth

Growth history is still building. We hold one measurement for this entry. A trend needs at least two, taken days apart — so this will fill in on its own rather than being estimated. We do not publish numbers we have not measured.

Measurement log — every players count we have recorded
Measured (UTC)PlayersChange
10 Aug 2026, 14:210first reading

Concurrent players at each of our measurements (insert-on-change).

Monetization — game passes

x2 Experience
199 R$
x2 Cash
149 R$
Auto-Cut
99 R$

Game passes for sale and their prices, measured from the Roblox passes API. Developer products bought inside the game aren’t publicly listable.

Badge funnel

Welcome! · welcome1,243,080 · 100.0% win
Cut in Line · other763,754 · 100.0% win
First in Line · other433,474 · 100.0% win
Boss Fight! · progression343,020 · 100.0% win

How many players have ever earned each badge — a proxy for how far players get before they stop. Steep drop-offs mark where a game loses people. Win rate is the past-day share of players who earned it; categories are assigned by a language model from each badge’s own name and description.