Open-source alternatives to Btsk

Open WebUI is a self-hosted, web-based platform designed for interacting with local and remote artificial intelligence models. It functions as a unified interface and orchestration suite, enabling users to build, deploy, and manage specialized AI agents equipped with custom instructions, external tool access, and private knowledge bases. The platform distinguishes itself through a modular architecture that supports complex AI workflows. It features a plugin-based framework for custom logic and pipeline-based request processing, allowing developers to filter or transform data streams before th

GoLearn is a machine learning library for the Go programming language. It provides a supervised learning framework and a toolkit for building, training, and evaluating predictive models through a standardized interface. The project implements a data frame system that loads CSV files into structured grids for matrix operations. It includes a preprocessing library for discretizing continuous variables and a model evaluation toolkit that utilizes confusion matrices and cross-validation to measure precision and recall. The library covers data engineering and management, including the ability to

This repository serves as a structured educational resource for machine learning and data science, providing a centralized collection of tutorials, lecture notes, and implementation guides. It is designed to support self-directed learning by organizing complex technical concepts into a clear, hierarchical path that spans from foundational statistical methods to advanced deep learning architectures. The project distinguishes itself through a comprehensive approach to skill development, bridging the gap between theoretical algorithmic foundations and functional software applications. It offers

Kaldi is an automatic speech recognition toolkit used to train and deploy models that convert spoken audio into text. It functions as a framework for designing and evaluating acoustic and language models through a structured pipeline of processing tools. The system acts as a cross-platform speech engine, capable of compiling recognition logic for Android and WebAssembly to enable execution on mobile devices and web browsers. It also includes a dedicated converter for migrating speech recognition models from the HTK format into a compatible internal structure. The toolkit covers a broad range

This project is a collection of interactive Jupyter notebooks and a structured machine learning tutorial series. It serves as an educational resource for studying predictive modeling and statistical analysis through a curriculum of executable code examples. The notebooks are specifically designed to accompany video tutorials, integrating external video assets with live code to synchronize visual instruction with hands-on experimentation. This approach allows users to follow sequential lessons while executing and modifying machine learning workflows directly in a browser. The content covers t

CNTK is a deep learning toolkit used for the design, construction, and training of neural networks. It defines model architectures as computational graphs and optimizes network parameters using an automatic differentiation engine and stochastic gradient descent. The project emphasizes large scale model distribution, spreading training workloads across multiple hardware nodes and GPUs. It features specialized support for dynamic sequence handling, allowing filters to be convolved across both spatial and dynamic sequence axes to process data of variable lengths. The toolkit provides hardware-a

oneDNN is a cross-architecture compute library and hardware acceleration framework designed as a oneAPI deep learning library. It functions as a neural network inference engine that provides optimized primitives to accelerate deep learning operations across diverse CPU and GPU architectures. The project distinguishes itself through a combination of just-in-time instruction generation based on detected processor features and microarchitecture-specific tuning. It utilizes graph-based operation compilation to minimize overhead and manages layout-aware tensors to optimize data access patterns acr

A Modern C++ Wrapper for TensorFlow

Distributed machine learning platform

OpenCV is a comprehensive computer vision library designed for real-time performance and cross-platform deployment. It provides a native execution environment that leverages multi-threaded operations and automated memory management to handle intensive computational tasks, including image processing and machine learning model inference. The library distinguishes itself through a data-oriented matrix framework that utilizes proxy-based array abstractions to provide a consistent interface for multidimensional data. By employing factory-pattern algorithm interfaces and runtime type dispatching, i

tiny-dnn is a header-only C++14 deep learning framework for building, training, and running inference on neural networks. It constructs static computational graphs at compile time using template-based layer composition, with a gradient-based backpropagation engine and minibatch stochastic gradient descent for training, all without external dependencies beyond the C++14 standard library. The framework supports importing pre-trained models from the Caffe framework directly, parsing its binary serialization format without requiring external protocol buffer libraries. It provides CPU-optimized te

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

simple neural network library in ANSI C

Flashlight is a standalone C++ machine learning library and tensor library used for building and training neural networks. It functions as a comprehensive neural network framework and automatic differentiation engine, providing the tools to construct computation graphs and calculate gradients via backpropagation. The project serves as a distributed training framework, utilizing all-reduce operations to synchronize gradients and parameters across multiple compute nodes and devices. It distinguishes itself through deep integration of high-performance tensor manipulation, native device memory in

Gorgonia is a Go library that provides an automatic differentiation engine and a computation graph framework for building and training neural networks. It functions as a CUDA-accelerated tensor library and a SIMD-optimized math library, enabling machine learning workflows entirely within the Go ecosystem. The library distinguishes itself through a dual-backend architecture that dispatches neural network operations to either a GPU or CPU depending on CUDA availability at runtime. It constructs differentiable directed acyclic graphs of tensor operations, supports reverse-mode automatic gradient

LangChain4j is a framework and library for building applications powered by large language models on the JVM. It provides a unified API for developing AI agents, implementing retrieval augmented generation, and integrating generative AI capabilities into professional software built with frameworks like Spring Boot or Quarkus. The project enables the creation of autonomous agents that can reason through tasks, manage memory, and execute external tools to achieve specific goals. It differentiates itself through a unified model interface that allows developers to switch between multiple model pr

This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks. The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin

MindsDB is an AI-native database engine that treats machine learning models and autonomous agents as virtual tables. By mapping external data sources, predictive models, and third-party services directly into the database schema, it enables users to perform inference, data retrieval, and complex orchestration using standard SQL syntax. The platform distinguishes itself through an autonomous agent orchestrator that executes iterative reasoning loops, allowing agents to plan data access and synthesize natural language responses from connected knowledge bases. It functions as a federated data ga

Ollama provides a framework for running and managing local machine learning models. It includes a command-line interface for model lifecycle management, such as creation, embedding generation, and configuration, alongside a stable API for programmatic interaction across multiple programming languages. The platform supports the import of models and adapters in various formats, including GGUF and Safetensors. Users can define custom model behaviors, prompt templates, and system messages through a configuration file format. It also offers tools for fine-tuning models with LoRA adapters and apply

A GPU (CUDA) based Artificial Neural Network library

PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui

RecastNavigation is a navigation mesh toolset and game AI pathfinding library used to generate and query walkable surfaces in 3D environments. It functions as a runtime navigation mesh generator and spatial query library, transforming raw 3D geometry into simplified surfaces to enable AI movement. The project includes a crowd simulation engine that manages steering and collision avoidance for large numbers of agents. It provides capabilities for group movement coordination and the simulation of many agents simultaneously using scalable navigation techniques. The system covers mesh generation

A universal RPC layer for AI agents. Connect to any function, any language, any framework, in minutes.

Curated list: Resources for machine learning in Ruby

A TypeScript implementation of a Model Context Protocol (MCP) server that integrates with PiAPI's API. PiAPI makes user able to generate media content with Midjourney/Flux/Kling/LumaLabs/Udio/Chrip/Trellis directly from Claude or any other MCP-compatible apps.

DocsGPT is a retrieval-augmented generation platform and private knowledge base used to build AI agents that perform grounded search and analysis. It functions as a multi-model AI orchestrator and enterprise agent builder, allowing for the integration of various local and cloud language models to customize reasoning and text generation. The project provides a visual environment for developing automated assistants using conditional logic and third-party API connectivity. It enables the creation of private AI agents capable of performing enterprise search and detailed document analysis using pr