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Forsaken: Endless Retry

Forsaken: Endless Retry

A Roblox experience owned by Vocal Void Studios.

HorrorENasymmetric multiplayerhorroralpha

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

Forsaken Retry Testing! THIS GAME IS IN ALPHA. EXPECT CHANGES, BUGS, AND UNFINISHED CONTENT. You were never supposed to return here. Yet every retry pulls you back into the same collapsing world. Enter a fractured realm where familiar figures reappear in unfamiliar ways—sometimes allies, sometimes enemies, sometimes something entirely different. Each attempt shifts the outcome, but nothing is ever certain. SURVIVORS: Work together, complete the objectives, and hold out until the timer reaches zero. Every retry offers another chance, but not a promise. KILLERS: Hunt them down. Break the loop. Ensure no survivor escapes your grasp. Once you enter, the cycle begins. And within these walls, you are not simply trapped. You are forsaken, over and over again. Forsaken Dev team if you wish to me to take this down contact me! Please like and favorite the game for more updates and skins.

Last updated by the developer 9 Aug 2026, 07:15 UTC.

0
playing (measured 10 Aug 2026, 16:36 UTC)
344,449
lifetime visits
53%
liked (210 👍 / 183 👎)
31,900
favourites (92.6 per 1k visits)
4 September 2025
created (per Roblox)

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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 We do not publish numbers we have not measured.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, 16:360first reading

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

Badge funnel

Welcome to—... "Hell". · welcome95,901 · 0.00% 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.