Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/gamedev-skills/awesome-gamedev-agent-skills --skill unreal-behavior-treesAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
Unreal Behavior Trees
Author NPC decision-making in UE5 with Behavior Trees driven by a Blackboard: structure the tree with composites, gate branches with decorators, keep state current with services, and run it from an AIController. Targets UE 5.8.
When to use
- Use when building enemy/NPC AI: creating a
BT_/BB_asset pair, structuring
Selector/Sequence branches, adding decorators (conditions) and services (periodic updates), writing custom BTTask/BTService nodes, or wiring an AIController to run the tree.
- Use when the project has Behavior Tree (
BT_) and Blackboard (BB_) assets and an
AAIController.
When not to use: the concept* of AI (FSM vs BT vs steering, cross-engine) → game-ai. Pure navigation/pathing math is engine navmesh (BT's MoveTo uses it). Simple one-off logic may be cheaper as a small state machine than a full tree.
Behavior Tree or StateTree? UE 5.8 ships both, and Behavior Trees are not deprecated. StateTree is Epic's general-purpose hierarchical state machine: it combines BT-style selection with explicit States and Transitions. If the project already uses StateTree assets, or the AI is naturally a set of modes with clear transitions (patrol → alert → combat), check with the user before adding a Behavior Tree next to it. Keep using Behavior Trees where the project already has BT_/BB_ assets.
Core workflow
- Create the pair: a Blackboard (
BB_) holds typed keys (the AI's memory:TargetActor,
LastKnownLocation, bIsInvestigating); a Behavior Tree (BT_) references that Blackboard.
- Possess and run. An
AAIControllerpossesses the pawn and callsRunBehaviorTree(BT),
which also initializes the referenced Blackboard.
- Structure with composites. Selector runs children left→right until one succeeds
(priority/fallback: "attack, else chase, else patrol"). Sequence runs children until one fails (do-all: "move to cover → reload → peek"). Simple Parallel runs one main task alongside a secondary.
- Gate branches with Decorators that read Blackboard keys (e.g. "Has Target?" guards the
combat branch). Set Observer Aborts so the tree re-evaluates when the key changes.
- Keep the Blackboard current with Services attached to a branch — they tick periodically
(e.g. update TargetActor via a sight check) only while that branch is active.
- Do work in Tasks, which return
Succeeded,Failed, orInProgress(latent tasks like
MoveTo finish later).
- Verify with the Behavior Tree debugger during PIE — it highlights the running node and
shows live Blackboard values, so you see exactly which branch executes.
Patterns
1. AIController that runs the tree (C++)
void AEnemyAIController::OnPossess(APawn* InPawn)
{
Super::OnPossess(InPawn);
if (BehaviorTree) // UPROPERTY(EditAnywhere) TObjectPtr<UBehaviorTree>
RunBehaviorTree(BehaviorTree); // initializes & uses the Blackboard the BT references
}2. A priority tree (node structure)
ROOT
└── Selector (try combat, else investigate, else patrol)
├── Sequence [Decorator: Blackboard 'TargetActor' Is Set, Observer Aborts: Both]
│ ├── Task: MoveTo (TargetActor) // latent: returns InProgress then Succeeded
│ └── Task: Attack
├── Sequence [Decorator: 'LastKnownLocation' Is Set]
│ ├── Task: MoveTo (LastKnownLocation)
│ └── Task: Wait (3s) + clear key
└── Task: Patrol (BTTask_FindPatrolPoint -> MoveTo)Observer Aborts: Both makes the combat branch interrupt patrol the instant TargetActor is set, and bail out when it's cleared — this is what makes the AI feel reactive.
3. Updating the Blackboard from code (e.g. on seeing the player)
void AEnemyAIController::SetTarget(AActor* Target)
{
if (UBlackboardComponent* BB = GetBlackboardComponent())
BB->SetValueAsObject(TEXT("TargetActor"), Target); // key name must match the BB asset
}
// Clear with BB->ClearValue(TEXT("TargetActor")); to drop back to a lower-priority branch.Pitfalls
- AI never starts — the pawn isn't possessed (set the Pawn's Auto Possess AI to "Placed
in World or Spawned" and assign the AIController), or RunBehaviorTree was never called.
- `MoveTo` instantly fails — no NavMesh in the level (add a Nav Mesh Bounds Volume), or the
target is off the navmesh.
- Branch doesn't react to changes — the gating Decorator's Observer Aborts is set to
None; set it to Self/Lower Priority/Both so the tree re-evaluates when the key changes.
- A task hangs the tree — a custom task returned
InProgressand never calls
FinishLatentTask. Always complete latent tasks.
- AI behaves strangely only when several NPCs share a tree — a custom task stores run state
in member variables. Task nodes are shared by every AI using the asset unless instanced; keep per-AI state in node memory (see the reference).
- Blackboard key typos —
SetValueAsObject("Taget", ...)silently does nothing; match the
key name and type exactly, or use a cached FBlackboardKeySelector.
- Sequence vs Selector confusion — Sequence = AND (stops on first failure); Selector = OR
(stops on first success). Swapping them inverts the behaviour.
References
- For a custom C++ `UBTTaskNode` (instant and latent
ExecuteTaskreturningEBTNodeResult,
per-AI node memory, AbortTask, and a FBlackboardKeySelector), read references/custom-bttask.md.
- Primary docs: "Behavior Trees in Unreal Engine"
(https://dev.epicgames.com/documentation/en-us/unreal-engine/behavior-trees-in-unreal-engine).
Related skills
game-ai— engine-agnostic AI design (FSM, BT, steering, pathfinding choices).unreal-cpp-gameplay— the AIController and pawn classes in C++.fps-shooter/tower-defense— genres that compose enemy AI.

