AIRS is a collection of open-source software tools, datasets, and benchmarks focused on Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. The goal is to develop and maintain an integrated, open, reproducible, and sustainable set of resources to advance the field of AI for Science. The current resources include tools for Quantum Mechanics, Density Functional Theory, Small Molecules, Protein Science, Materials Science, Molecular Interactions, and Partial Differential Equations.

OmniGibson is a platform for accelerating Embodied AI research built upon NVIDIA's Omniverse platform. It features photorealistic visuals, physical realism, fluid and soft body support, large-scale high-quality scenes and objects, dynamic kinematic and semantic object states, mobile manipulator robots with modular controllers, and an OpenAI Gym interface. The platform provides a comprehensive environment for researchers to conduct experiments and simulations in the field of Embodied AI.

Flower is a framework for building federated learning systems. It is designed to be customizable, extensible, framework-agnostic, and understandable. Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand.

LibreChat is an all-in-one AI conversation platform that integrates multiple AI models, including ChatGPT, into a user-friendly interface. It offers a wide range of features, including multimodal chat, multilingual UI, AI model selection, custom presets, conversation branching, message export, search, plugins, multi-user support, and extensive configuration options. LibreChat is open-source and community-driven, with a focus on providing a free and accessible alternative to ChatGPT Plus. It is designed to enhance productivity, creativity, and communication through advanced AI capabilities.

rLLM (relationLLM) is a Pytorch library for Relational Table Learning (RTL) with LLMs. It breaks down state-of-the-art GNNs, LLMs, and TNNs as standardized modules and facilitates novel model building in a 'combine, align, and co-train' way using these modules. The library is LLM-friendly, processes various graphs as multiple tables linked by foreign keys, introduces new relational table datasets, and is supported by students and teachers from Shanghai Jiao Tong University and Tsinghua University.

MemOS is an operating system for Large Language Models (LLMs) that enhances them with long-term memory capabilities. It allows LLMs to store, retrieve, and manage information, enabling more context-aware, consistent, and personalized interactions. MemOS provides Memory-Augmented Generation (MAG) with a unified API for memory operations, a Modular Memory Architecture (MemCube) for easy integration and management of different memory types, and multiple memory types including Textual Memory, Activation Memory, and Parametric Memory. It is extensible, allowing users to customize memory modules, data sources, and LLM integrations. MemOS demonstrates significant improvements over baseline memory solutions in multiple reasoning tasks, with a notable improvement in temporal reasoning accuracy compared to the OpenAI baseline.

FastDeploy is an inference and deployment toolkit for large language models and visual language models based on PaddlePaddle. It provides production-ready deployment solutions with core acceleration technologies such as load-balanced PD disaggregation, unified KV cache transmission, OpenAI API server compatibility, comprehensive quantization format support, advanced acceleration techniques, and multi-hardware support. The toolkit supports various hardware platforms like NVIDIA GPUs, Kunlunxin XPUs, Iluvatar GPUs, Enflame GCUs, and Hygon DCUs, with plans for expanding support to Ascend NPU and MetaX GPU. FastDeploy aims to optimize resource utilization, throughput, and performance for inference and deployment tasks.

Awesome Quant AI is a curated list of resources focusing on quantitative investment and trading strategies using artificial intelligence and machine learning in finance. It covers key challenges in quantitative finance, AI/ML technical fit, predictive modeling, sequential decision-making, synthetic data generation, contextual reasoning, mathematical foundations, design approach, quantitative trading strategies, tools and platforms, learning resources, books, research papers, community, and conferences. The repository aims to provide a comprehensive resource for those interested in the intersection of AI, machine learning, and quantitative finance, with a focus on extracting alpha while managing risk in financial systems.

Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.

Hunyuan-T1 is a cutting-edge large-scale hybrid Mamba reasoning model driven by reinforcement learning. It has been officially released as an upgrade to the Hunyuan Thinker-1-Preview model. The model showcases exceptional performance in deep reasoning tasks, leveraging the TurboS base and Mamba architecture to enhance inference capabilities and align with human preferences. With a focus on reinforcement learning training, the model excels in various reasoning tasks across different domains, showcasing superior abilities in mathematical, logical, scientific, and coding reasoning. Through innovative training strategies and alignment with human preferences, Hunyuan-T1 demonstrates remarkable performance in public benchmarks and internal evaluations, positioning itself as a leading model in the field of reasoning.

ABI (Agentic Brain Infrastructure) is a Python-based AI Operating System designed to serve as the core infrastructure for building an Agentic AI Ontology Engine. It empowers organizations to integrate, manage, and scale AI-driven operations with multiple AI models, focusing on ontology, agent-driven workflows, and analytics. ABI emphasizes modularity and customization, providing a customizable framework aligned with international standards and regulatory frameworks. It offers features such as configurable AI agents, ontology management, integrations with external data sources, data processing pipelines, workflow automation, analytics, and data handling capabilities.

Chatbox is a desktop client for ChatGPT, Claude, and other LLMs, providing features like local data storage, multiple LLM provider support, image generation, enhanced prompting, keyboard shortcuts, and more. It offers a user-friendly interface with dark theme, team collaboration, cross-platform availability, web version access, iOS & Android apps, multilingual support, and ongoing feature enhancements. Developed for prompt and API debugging, it has gained popularity for daily chatting and professional role-playing with AI assistance.

Kelivo is a Flutter LLM Chat Client with modern design, dark mode, multi-language support, multi-provider support, custom assistants, multimodal input, markdown rendering, voice functionality, MCP support, web search integration, prompt variables, QR code sharing, data backup, and custom requests. It is built with Flutter and Dart, utilizes Provider for state management, Hive for local data storage, and supports dynamic theming and Markdown rendering. Kelivo is a versatile tool for creating and managing personalized AI assistants, supporting various input formats, and integrating with multiple search engines and AI providers.

Chatbox is a desktop client for ChatGPT, Claude, and other LLMs, providing a user-friendly interface for AI copilot assistance on Windows, Mac, and Linux. It offers features like local data storage, multiple LLM provider support, image generation with Dall-E-3, enhanced prompting, keyboard shortcuts, and more. Users can collaborate, access the tool on various platforms, and enjoy multilingual support. Chatbox is constantly evolving with new features to enhance the user experience.

The ros-mcp-server repository contains a ROS (Robot Operating System) package that provides a server for Multi-Contact Planning (MCP) in robotics. The server facilitates the planning of multiple contacts between a robot and its environment, enabling complex manipulation tasks. It includes functionalities for collision checking, motion planning, and contact stability analysis. This tool is designed to enhance the capabilities of robotic systems by enabling them to perform tasks that involve multiple points of contact with the environment. The repository includes documentation and examples to help users integrate the MCP server into their robotic applications.

GPTSwarm is a graph-based framework for LLM-based agents that enables the creation of LLM-based agents from graphs and facilitates the customized and automatic self-organization of agent swarms with self-improvement capabilities. The library includes components for domain-specific operations, graph-related functions, LLM backend selection, memory management, and optimization algorithms to enhance agent performance and swarm efficiency. Users can quickly run predefined swarms or utilize tools like the file analyzer. GPTSwarm supports local LM inference via LM Studio, allowing users to run with a local LLM model. The framework has been accepted by ICML2024 and offers advanced features for experimentation and customization.

AIRS is a collection of open-source software tools, datasets, and benchmarks focused on Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. The goal is to develop and maintain an integrated, open, reproducible, and sustainable set of resources to advance the field of AI for Science. The current resources include tools for Quantum Mechanics, Density Functional Theory, Small Molecules, Protein Science, Materials Science, Molecular Interactions, and Partial Differential Equations.

AI2BMD is a program for efficiently simulating protein molecular dynamics with ab initio accuracy. The repository contains datasets, simulation programs, and public materials related to AI2BMD. It provides a Docker image for easy deployment and a standalone launcher program. Users can run simulations by downloading the launcher script and specifying simulation parameters. The repository also includes ready-to-use protein structures for testing. AI2BMD is designed for x86-64 GNU/Linux systems with recommended hardware specifications. The related research includes model architectures like ViSNet, Geoformer, and fine-grained force metrics for MLFF. Citation information and contact details for the AI2BMD Team are provided.

SciMLBenchmarks.jl holds webpages, pdfs, and notebooks showing the benchmarks for the SciML Scientific Machine Learning Software ecosystem, including: * Benchmarks of equation solver implementations * Speed and robustness comparisons of methods for parameter estimation / inverse problems * Training universal differential equations (and subsets like neural ODEs) * Training of physics-informed neural networks (PINNs) * Surrogate comparisons, including radial basis functions, neural operators (DeepONets, Fourier Neural Operators), and more The SciML Bench suite is made to be a comprehensive open source benchmark from the ground up, covering the methods of computational science and scientific computing all the way to AI for science.

SciML.ai is an open source software organization dedicated to unifying packages for scientific machine learning. It focuses on developing modular scientific simulation support software, including differential equation solvers, inverse problems methodologies, and automated model discovery. The organization aims to provide a diverse set of tools with a common interface, creating a modular, easily-extendable, and highly performant ecosystem for scientific simulations. The website serves as a platform to showcase SciML organization's packages and share news within the ecosystem. Pull requests are encouraged for contributions.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.