Ai Framework
Showing 33 of 33 projects
A cutting-edge framework for training and deploying state-of-the-art YOLO models for object detection, segmentation, classification, and pose estimation.
A framework for programming language models with Python instead of prompting, enabling modular AI systems with automatic prompt optimization.
An experimental Python framework for building self-building autonomous agents with a function management system and dashboard.
A cross-platform, high-performance accelerator for machine learning inference and training with ONNX models.
An all-in-one AI framework for semantic search, LLM orchestration, and language model workflows built around an embeddings database.
A prompting language for building reliable AI workflows and agents with type-safe, structured outputs across multiple programming languages.
A lightweight, modular, and scalable deep learning framework built on the original Caffe.
Open-source framework for building full-stack AI-powered applications with unified APIs across JavaScript, Go, and Python.
Open-source framework for building full-stack AI-powered applications with unified APIs for multiple languages and model providers.
An end-to-end framework for building custom AI applications and agents directly integrated with databases.
A C++17 library for creating flexible, reactive Behavior Trees, primarily for robotics and game AI.
A comprehensive Python-first reinforcement learning framework with modular abstractions for decision intelligence applications.
A Godot 4 plugin providing Behavior Trees and Hierarchical State Machines for creating complex AI behaviors in games.
TensorFlow port for AMD GPUs via ROCm, enabling machine learning on Radeon hardware.
A unified deep learning and reinforcement learning framework supporting multiple backends and hardware platforms.
A C# library for implementing behavior trees in game AI, providing a modular framework for creating complex NPC behaviors.
A Go implementation of neural networks including BackPropagation, RBF, and Perceptron networks with parallel processing capabilities.
A pure C99 ONNX runtime with zero dependencies, designed for embedded devices and old hardware.
A collection of community-built plugins for Firebase Genkit, extending support to various AI models, vector stores, and workflow tools.
A lightweight C++ behavior tree library with a QT5 remote debugger and optional Lua bindings for AI logic.
A curated list of resources, plugins, tools, and examples for the Genkit AI framework ecosystem.
A flexible deep learning framework for Ruby, ported from Python's Chainer.
A Go module implementing multi-layer neural networks for machine learning tasks.
Ruby bindings for the Apache MXNet deep learning framework, enabling Ruby developers to build and train neural networks.
A V programming language module for creating and training multi-layer neural networks with backpropagation.
A functional behavior tree implementation in Lua for game AI and entity behavior modeling.
A behavior tree implementation for ActionScript 3, ported from the original Objective-C version.
A Java implementation of the Genkit framework for building observable, AI-powered applications with multi-model support.
A Java implementation of the Genkit framework for building observable, traceable AI-powered applications with multi-model support.
A simple yet powerful behavior tree implementation for game AI, with built-in tree visualization for LÖVE.
A Redis plugin for GenKit that provides state storage, trace storage, caching, and rate limiting capabilities.
A Ruby library for designing, processing, and training artificial neural networks.
A community plugin for integrating Weaviate vector database with Google's Genkit AI framework.
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