Awesome GitHub RepositoriesModule Composition

DSPy is a declarative programming framework designed for building complex language model applications. It treats model interactions as modular, composable programs, allowing developers to define task logic through typed class schemas rather than relying on manually written prompts. By organizing workflows into hierarchical, reusable Python objects, the framework enables the construction of sophisticated AI systems that manage state and execution flow independently. The framework distinguishes itself through an automated optimization engine that iteratively refines prompt instructions and few-

Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep learning models. It functions as a system for composing neural networks from reusable modules and layers that encapsulate their own parameters and internal states. The project provides specialized tools for distributed model training, enabling the synchronization of gradients across multiple hardware devices. It also serves as a model state management system, allowing for the persistence of neural network weights and the export of portable models that separate the computation g

Hardhat is a smart contract development framework and EVM tooling suite designed for the full lifecycle of Ethereum and EVM-compatible applications. It serves as a development environment for compiling, testing, and deploying smart contracts, providing a local blockchain simulation and a programmable task runner. The framework is distinguished by its extensive simulation capabilities, including the ability to fork remote network state and manipulate block time or account balances. It features a hook-based plugin system that allows for the extension of core functionality and the creation of cu

Flax is a deep learning framework and JAX neural network library designed for building complex machine learning models. It functions as a distributed training library and model state manager, providing a toolkit for defining flexible neural network architectures and scaling their training across multiple hardware devices. The project is characterized by a design that separates network logic from parameter values to remain compatible with pure functions. It uses hierarchical module composition to organize networks as trees of nested modules and employs a reference-based state management system

Maddy is a modular mail server that assembles a complete email system by connecting small, single-purpose modules through a declarative configuration file. Rather than a monolithic stack, it lets operators compose message processing, storage, authentication, and security enforcement from interchangeable building blocks, with each module handling a specific function like receiving SMTP connections, verifying credentials, or applying policy checks. The server distinguishes itself through its flexible authentication and security architecture. It delegates user verification to external systems in

Flashlight ist eine C++-Bibliothek für maschinelles Lernen und ein Deep-Learning-Framework zur Erstellung und zum Training neuronaler Netze. Es fungiert als Tensor-Manipulationsbibliothek und Engine für automatische Differenzierung, die Operationen verfolgt, um Gradienten via Backpropagation für die Modelloptimierung zu berechnen. Das Projekt zeichnet sich durch seine Rolle als Framework für verteiltes Training aus, das All-Reduce-Gradientensynchronisation und verteilte Umgebungen nutzt, um Machine-Learning-Workloads über mehrere Nodes und Geräte hinweg zu skalieren. Es verfügt über eine Backend-agnostische Speicherschnittstelle und RAII-basiertes Management, um Tensor-Operationen von der physischen Hardware zu entkoppeln. Das Framework deckt ein breites Funktionsspektrum ab, einschließlich der Konstruktion neuronaler Netzwerkarchitekturen mit konvolutiven, linearen und rekurrenten Schichten. Es bietet umfangreiche Utilities für Tensor-Algebra, Dataset-Management und Batching, versionierte Binärserialisierung für Modellzustände sowie Überwachungswerkzeuge zur Verfolgung von Trainingsmetriken und Speicherauslastung.

Dora is a robotics dataflow framework and distributed orchestrator used to build and manage processing pipelines. It enables the deployment of robotics workloads across clusters with remote node execution and provides a real-time data pipeline for predictable performance. The system is distinguished by its support for multi-language nodes written in Rust, Python, C, or C++ that interoperate within a single dataflow. It utilizes a zero-copy shared-memory transport and columnar formats to minimize latency for large payloads, and it includes bidirectional bridges to integrate with external ecosy