Nextbots in Roblox: What They Are and Why Your Server Keeps Crashing
Nextbots are basically AI-controlled entities that chase players through Roblox maps using pathfinding. The mechanic is simple on paper, which is why every developer from ten years ago to last week has made their own version. The Nicos Nextbots Wiki Everything You Need To Know About The Creepy Roblox Sensation covers a lot of ground, but the actual implementation side is where things get messy fast. I spent a solid month debugging a nextbot system for a group game, and the issue wasn't the chase logic itself. It was raycast spam. Each bot was firing forty raycasts per frame to detect walls, and by the time five bots were active on a busy server, the client was dropping frames down to twenty. The workaround was switching to spatial partitioning with a grid-based collision cache that updated every three frames instead of continuously. That dropped the raycast count by roughly eighty-five percent without making the bots look any less responsive to human observers.
The Nicos Nextbots Wiki Everything You Need To Know About The Creepy Roblox Sensation
The wiki itself is community-maintained, which means coverage is uneven. Some entries have precise script references and frame-perfect timing data. Others are just screenshots with a paragraph of fan speculation. I've found the most reliable entries are the ones linked to specific Place IDs rather than general theory posts. When you're troubleshooting a specific bot behavior, searching by Place ID gets you to the actual implementation faster than reading three pages of lore. Common nextbot types you will encounter: Chasers use PathfindingService with NavMesh. Most people assume this is the only way, but NavMesh fails completely in maps with gaps, moving platforms, or non-uniform floor heights. In those cases, a simple A* algorithm on a custom grid overlay performs better and is easier to debug when something goes wrong. I switched an entire chaser class from PathfindingService to A* on a 2-unit grid and cut average path recalculation time from 40 milliseconds to under 8 milliseconds per update cycle.
Teleporters spawn in front of or behind the player based on line-of-sight checks. The pitfall here is that most implementations use DistanceTo + HumanoidRootPart CFrame dot products, which breaks when the map has thin walls or partial occlusion. The fix is to add a Raycast or Region3 check before teleporting. Without it, bots will teleport through solid geometry and appear inside walls, which looks broken and confuses players immediately. Sound-based nextbots react to footsteps or weapon fire. These are harder to balance because Roblox's sound propagation doesn't scale linearly with distance in a way that feels fair. I ran tests where a shotgun shot at max range was detected three times farther away than a knife slash, which made long-range combat trivial against sound bots and impossible at close range. The solution was capping detection radius per sound type and adding a randomness factor of plus or minus fifteen percent to the detection threshold so the behavior isn't perfectly predictable.
Get the Full Details
Building a Basic Nextbot System
Start with a ModuleScript that defines the bot's state machine. Keep it separate from your ServerScriptService loop. If you put everything in one script, you will lose track of which variables are shared between instances and which are local. The state machine needs at least four states: patrol, chase, search, and idle. Patrol moves the bot along predefined waypoints. Chase activates when the bot detects a player. Search plays when the bot loses the player temporarily. Idle resets the bot to patrol after a timeout. This seems obvious but most first implementations skip the search state entirely and just snap back to patrol, which makes bots feel janky. For movement, do not use RunService.RenderStepped for bot logic. Use Heartbeat or a custom timer at six-tenths of a second intervals. RenderStepped runs at display refresh rate, which means on a 144Hz monitor your bot updates nearly three times as often as it should, and on a 60Hz monitor it updates at half the expected rate. A fixed timestep keeps behavior consistent across all hardware.
Here is what the basic chase loop looks like in practice: Create a proximity check using magnitude or |Vector3.Magnitude|. When it drops below your threshold, set the state to chase and assign the nearest player as the target. Move the bot toward the target's HumanoidRootPart using a Velocity or LinearVelocity constraint on the PrimaryPart. Stop the velocity when the bot reaches the target or when the target exits the detection radius for more than two seconds. The two-second timeout is critical. Without it, bots will chase players who are hiding behind cover indefinitely, which bogs down the server and makes the game unplayable after five minutes.
Performance Numbers That Matter
A single well-optimized nextbot on a standard Roblox map consumes roughly 2 to 4 milliseconds of server time per update tick. That sounds small until you realize fifteen bots running simultaneously add up to sixty to one hundred twenty milliseconds per tick, which is half a frame at sixty frames per second. If you add path recalculation on top of that, you are looking at serious lag on lower-tier servers. The bottleneck is almost always the pathfinding or detection loop, not the movement itself. Using a simplified movement approach like body velocity toward a predicted position instead of full NavMesh recalculation every frame can reduce per-bot server overhead by approximately seventy percent. The tradeoff is slightly less precise navigation around obstacles, but in practice players cannot tell the difference at normal chase distances.
What the Wiki Gets Wrong
Some entries recommend using :GetChildren() on every Part in the map to check for collidable surfaces. This is terrible advice for anything larger than a small test arena. On a mid-size map with five hundred parts, that call alone takes three to five milliseconds per frame just to enumerate the tree. Use workspace:GetPartsInRegion3 or the newer Region3WithDescendants instead, which queries the physics engine directly and is roughly ten times faster on maps with more than two hundred parts. Another common error is storing the last known player position in a global variable shared across all bot instances. When Bot A loses sight of a player, it writes the position to the shared variable. Bot B reads it and moves there, even though Bot B never saw the player. This causes bots to congregate on stale positions and waste time searching empty areas. Each bot should maintain its own lastKnownPosition table keyed by player ID.
Where to Find Working Examples
The wiki links to several group games that use nextbot systems. The most functional ones I have tested are the early builds from 2021 through 2023, where pathfinding was less aggressive and detection ranges were more balanced. Later versions tend to add more features but also more bugs, especially around simultaneous bot spawning and desync between client prediction and server authority. If you want to study a clean implementation, look for games that use a single authority model where the server determines all bot positions and the client only renders them. Peer-to-peer or client-authoritative nextbot systems introduce interpolation errors that make bots appear to teleport or freeze, and fixing those issues takes significantly more work than starting with server-side authority from the beginning.
When Nextbots Are the Wrong Tool
Nextbots work well for horror maps and chase-oriented experiences. They do not work well for competitive shooters where player skill should dominate over AI behavior. If your game requires precise aim and tactical positioning, a nextbot that reacts purely on detection radius will feel cheap because it removes agency from the player. In those cases, consider using scripted event triggers or a behavior tree system that responds to player actions rather than raw proximity. There is also the question of map design. Nextbots require clear sightlines and predictable geometry to function properly. Open maps with verticality and multiple floors cause constant pathfinding failures unless you invest heavily in custom NavMesh baking for each zone. If you are building a multi-floor map and do not want to spend hours tuning NavMesh data, stick to simpler detection methods or limit bot count to three or fewer per zone. The core concept is straightforward enough that anyone can clone a template and publish a game within an hour. The part that takes actual time is making it run smoothly under load and behave consistently across different devices. The Nicos Nextbots Wiki gives you a decent starting point, but the real learning happens when your server starts choking on raycasts and you have to figure out why your bots keep teleporting through walls.
