Godot Steering AI Framework is a specialized extension for the Godot game engine that empowers developers to equip NPCs, enemies, and autonomous characters with lifelike movement and decision-making patterns. By exposing a set of prebuilt steering behaviors and combining them through weighted blending, users can achieve smooth path following, dynamic obstacle avoidance, group formation, and responsive pursuit or evasion. The framework simplifies AI-driven navigation, allowing you to focus on gameplay mechanics rather than low-level movement code, and supports both 2D and 3D projects with minimal setup.
Godot Steering AI Framework Core Features
Seek, Flee, and Arrival behaviors
Wander and Path Following
Obstacle Avoidance
Separation, Cohesion, and Alignment (Flocking)
Weighted behavior blending
2D and 3D support
Runtime customizable parameters
Easy integration with Godot scenes
Godot Steering AI Framework Pro & Cons
The Cons
Not an AI tool or agent but rather a game-specific AI movement framework.
No explicit details on community support or frequent updates.
Does not include AI decision-making or learning components.
No pricing or marketplace information available.
No direct links to source code or repositories provided.
The Pros
Inspired by a proven AI steering framework (GDX-AI).
Integrates seamlessly with Godot engine.
Allows for modular and flexible character movement behaviors.
Supports multiple physics body types in Godot.
Well-documented with code references and installation guides.
AgentSimulation is an open-source Python library built on Pygame for simulating multiple autonomous agents in a 2D environment. It allows users to configure agent properties, steering behaviors (seek, flee, wander), collision detection, pathfinding, and interactive rules. With real-time rendering and modular design, it supports rapid prototyping, teaching simulations, and small-scale research in swarm intelligence or multi-agent interactions.
ePH-MAPF provides an efficient pipeline for computing collision-free paths for dozens to hundreds of agents on grid-based maps. It uses prioritized heuristics, incremental search techniques, and customizable cost metrics (Manhattan, Euclidean) to balance speed and solution quality. Users can select between different heuristic functions, integrate the library into Python-based robotics systems, and benchmark performance on standard MAPF scenarios. The codebase is modular and well-documented, enabling researchers and developers to extend it for dynamic obstacles or specialized environments.