Found 7 packages
This is a NuGet package of luxkun's work, ReGoap, a generic C# GOAP (Goal Oriented Action Planning) library, but without the Unity-specific part.
MediatR pipeline behavior for automatic Result handling in CQRS commands and queries. Seamlessly integrates Fox.ResultKit with MediatR for clean error handling in request pipelines.
Platform design is based on strong and well-defined borderline between procedural test cases structure and object-oriented code-behind. We think that test cases implementation approach (inside BDD paradigm) is literaly the same across different applications. That means that we can define and reuse grammar structures across different application domains. On top of this, while talking about single page web applications, we may take into account that atomic controls behavior is also the same across different domains. The whole automation code is divided into the following parts: - Feature files - Bindings - Wrappers - Infrastructure bindings - UI structure descriptive code - Supportive code
YoloDotNet is a modular, lightweight C# library for high-performance, real-time computer vision and YOLO-based inference in .NET. Built on .NET 8 and powered by ONNX Runtime, YoloDotNet provides explicit, production-ready inference for modern YOLO model families, including YOLOv5u through YOLOv26, YOLO-World, YOLO-E, and RT-DETR. The library features a fully modular execution architecture with pluggable execution providers for CPU, CUDA / TensorRT, Intel OpenVINO, Apple CoreML, and DirectML, enabling predictable deployment across Windows, Linux, and macOS. YoloDotNet intentionally avoids heavy computer vision frameworks such as OpenCV. Image handling and preprocessing are performed using SkiaSharp, with no Python runtime, no hidden preprocessing, and no implicit behavior. Designed for low-latency inference and long-running workloads, YoloDotNet gives developers full control over execution, memory usage, and preprocessing — allowing you to choose the hardware, platform, and execution backend without unnecessary abstraction or overhead.
The VersaTul Data Contracts project provides generic interfaces that are supported throughout the Data manipulating projects in the VersaTul ecosystem. These tend to be more database-oriented projects. Developers who may want to change the underline implementation of these contracts can create their own implementation of such contract and supply it to the VersaTul project in which they require to change the behavior.
The ABxM Framework provides an open platform for experimentation with agent-based, aka individual-based, systems. The aim of the framework is to "standardize" research equipment, in this case the tools for modeling and simulation, in order to increase transparency of agent-based models, and repeatability of research results. Its main application can be seen in the modeling and simulation of dynamic systems that can be conceived of as consisting of locally interacting, discrete entities that have autonomy and goal-orientation. These models and simulations can be explorative (divergent) or goal-oriented (convergent) as, for example, when used for optimization. The framework is composed of a set of class libraries organized around the core agent library ABxM.Core. The main usage scenario for domain-specific applications is to expand the functionality of the framework by building an add-on while using the core as the common infrastructure. The ABxM.Core consists of the agent core library ABxM.Core and an interoperability library for Rhino 6 and later versions. ABxM.Core implements the functionality specific to agent-based modelling and simulation. The core library can, in principle, be referenced from any application that is compatible with McNeel’s Rhino.Inside technology. ABxM.Core defines four base classes for behaviors, agents, agent systems, and environments, and the Solver class. In addition to the base classes, the core library provides implementations for Vector-based systems called “Boid” (in reference to Craig Reynolds’ Boids), point-based systems called “Cartesian”, matrix-based systems (2d and 3d), mesh systems, and network systems. The necessary agent, system, environment, and behavior classes are derived from the corresponding base classes.
YoloDotNet.ExecutionProvider.DirectML enables hardware-accelerated inference on Windows using Microsoft’s DirectML framework. This execution provider integrates ONNX Runtime’s DirectML backend and runs on top of DirectX 12, allowing inference to be accelerated on a wide range of GPUs using the Windows graphics driver stack. DirectML is a Windows-only technology and is supported on Windows 10 and Windows 11 with a DirectX 12–capable GPU. No vendor-specific SDKs or external runtimes are required beyond the standard Windows graphics drivers. This makes the DirectML execution provider a low-friction option for GPU acceleration on Windows systems. Designed for YoloDotNet’s modular, execution-provider-agnostic architecture, the DirectML provider integrates cleanly with the core library and exposes explicit, predictable inference behavior. It is well-suited for Windows applications that require GPU acceleration without locking into a specific hardware vendor or proprietary ML stack.