LiveKit is an open source project providing scalable, multi-user conferencing based on WebRTC. It offers a server written in Go, client SDKs, and advanced features like speaker detection, end-to-end encryption, and SVC codecs. The tool is easy to deploy with support for JWT authentication and robust networking. LiveKit ecosystem includes agents for AI applications, tools like CLI and Docker image, and SDKs for both client and server-side development.

Xorbits Inference (Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command. Whether you are a researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full potential of cutting-edge AI models.

Aichat is an AI-powered CLI chat and copilot tool that seamlessly integrates with over 10 leading AI platforms, providing a powerful combination of chat-based interaction, context-aware conversations, and AI-assisted shell capabilities, all within a customizable and user-friendly environment.

Audio Webui is a tool designed to provide a user-friendly interface for audio processing tasks. It supports automatic installers, Docker deployment, local manual installation, Google Colab integration, and common command line flags. Users can easily download, install, update, and run the tool for various audio-related tasks. The tool requires Python 3.10, Git, and ffmpeg for certain features. It also offers extensions for additional functionalities.

Dokugen is a lightweight README.md file Generator Command Line Interface Tool that simplifies the process of writing README.md files by generating professional READMEs for projects, saving time and ensuring consistency using AI. It automates the most neglected part of a repo, resulting in cleaner projects and happier contributors.

AIOS, a Large Language Model (LLM) Agent operating system, embeds large language model into Operating Systems (OS) as the brain of the OS, enabling an operating system "with soul" -- an important step towards AGI. AIOS is designed to optimize resource allocation, facilitate context switch across agents, enable concurrent execution of agents, provide tool service for agents, maintain access control for agents, and provide a rich set of toolkits for LLM Agent developers.

StarVector is a multimodal vision-language model for Scalable Vector Graphics (SVG) generation. It can be used to perform image2SVG and text2SVG generation. StarVector works directly in the SVG code space, leveraging visual understanding to apply accurate SVG primitives. It achieves state-of-the-art performance in producing compact and semantically rich SVGs. The tool provides Hugging Face model checkpoints for image2SVG vectorization, with models like StarVector-8B and StarVector-1B. It also offers datasets like SVG-Stack, SVG-Fonts, SVG-Icons, SVG-Emoji, and SVG-Diagrams for evaluation. StarVector can be trained using Deepspeed or FSDP for tasks like Image2SVG and Text2SVG generation. The tool provides a demo with options for HuggingFace generation or VLLM backend for faster generation speed.

SimAI is the industry's first full-stack, high-precision simulator for AI large-scale training. It provides detailed modeling and simulation of the entire LLM training process, encompassing framework, collective communication, network layers, and more. This comprehensive approach offers end-to-end performance data, enabling researchers to analyze training process details, evaluate time consumption of AI tasks under specific conditions, and assess performance gains from various algorithmic optimizations.

Intel® Extension for TensorFlow* is a high performance deep learning extension plugin based on TensorFlow PluggableDevice interface. It aims to accelerate AI workloads by allowing users to plug Intel CPU or GPU devices into TensorFlow on-demand, exposing the computing power inside Intel's hardware. The extension provides XPU specific implementation, kernels & operators, graph optimizer, device runtime, XPU configuration management, XPU backend selection, and options for turning on/off advanced features.

This repository contains a Telegram bot built with Golang that integrates with DeepSeek API to provide AI-powered responses. The bot supports streaming replies, making interactions feel more natural and dynamic. It offers features like AI responses, streaming output, command handling, and easy deployment. Users can configure the bot via environment variables for customization. The bot can be deployed locally or on a cloud server, and it supports custom commands and real-time responses.

Polaris establishes a novel, industry‑certified standard to foster the development of impactful methods in AI-based drug discovery. This library is a Python client to interact with the Polaris Hub. It allows you to download Polaris datasets and benchmarks, evaluate a custom method against a Polaris benchmark, and create and upload new datasets and benchmarks.

HIA (Health Insights Agent) is an AI agent designed to analyze blood reports and provide personalized health insights. It features an intelligent agent-based architecture with multi-model cascade system, in-context learning, PDF upload and text extraction, secure user authentication, session history tracking, and a modern UI. The tech stack includes Streamlit for frontend, Groq for AI integration, Supabase for database, PDFPlumber for PDF processing, and Supabase Auth for authentication. The project structure includes components for authentication, UI, configuration, services, agents, and utilities. Contributions are welcome, and the project is licensed under MIT.

Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

Palico AI is a tech stack designed for rapid iteration of LLM applications. It allows users to preview changes instantly, improve performance through experiments, debug issues with logs and tracing, deploy applications behind a REST API, and manage applications with a UI control panel. Users have complete flexibility in building their applications with Palico, integrating with various tools and libraries. The tool enables users to swap models, prompts, and logic easily using AppConfig. It also facilitates performance improvement through experiments and provides options for deploying applications to cloud providers or using managed hosting. Contributions to the project are welcomed, with easy ways to get involved by picking issues labeled as 'good first issue'.

FlowGram.AI is a node-based flow building engine that helps developers create workflows in fixed or free connection layout modes. It provides interaction best practices and is suitable for visual workflows with clear inputs and outputs. The tool focuses on empowering workflows with AI capabilities.

LiveKit is an open source project providing scalable, multi-user conferencing based on WebRTC. It offers a server written in Go, client SDKs, and advanced features like speaker detection, end-to-end encryption, and SVC codecs. The tool is easy to deploy with support for JWT authentication and robust networking. LiveKit ecosystem includes agents for AI applications, tools like CLI and Docker image, and SDKs for both client and server-side development.

HoloInsight is a cloud-native observability platform that provides low-cost and high-performance monitoring services for cloud-native applications. It offers deep insights through real-time log analysis and AI integration. The platform is designed to help users gain a comprehensive understanding of their applications' performance and behavior in the cloud environment. HoloInsight is easy to deploy using Docker and Kubernetes, making it a versatile tool for monitoring and optimizing cloud-native applications. With a focus on scalability and efficiency, HoloInsight is suitable for organizations looking to enhance their observability and monitoring capabilities in the cloud.

Metaso AI Free service supports high-speed streaming output, secret tower AI super network search (full network or academic as well as concise, in-depth, research three modes), zero-configuration deployment, multi-token support. Fully compatible with ChatGPT interface. It also has seven other free APIs available for use. The tool provides various deployment options such as Docker, Docker-compose, Render, Vercel, and native deployment. Users can access the tool for chat completions and token live checks. Note: Reverse API is unstable, it is recommended to use the official Metaso AI website to avoid the risk of banning. This project is for research and learning purposes only, not for commercial use.

Tribe AI is a low code tool designed to rapidly build and coordinate multi-agent teams. It leverages the langgraph framework to customize and coordinate teams of agents, allowing tasks to be split among agents with different strengths for faster and better problem-solving. The tool supports persistent conversations, observability, tool calling, human-in-the-loop functionality, easy deployment with Docker, and multi-tenancy for managing multiple users and teams.

Melodisco is an AI music player that allows users to listen to music and manage playlists. It provides a user-friendly interface for music playback and organization. Users can deploy Melodisco with Vercel or Docker for easy setup. Local development instructions are provided for setting up the project environment. The project credits various tools and libraries used in its development, such as Next.js, Tailwind CSS, and Stripe. Melodisco is a versatile tool for music enthusiasts looking for an AI-powered music player with features like authentication, payment integration, and multi-language support.

KB Builder is an open-source knowledge base generation system based on the LLM large language model. It utilizes the RAG (Retrieval-Augmented Generation) data generation enhancement method to provide users with the ability to enhance knowledge generation and quickly build knowledge bases based on RAG. It aims to be the central hub for knowledge construction in enterprises, offering platform-based intelligent dialogue services and document knowledge base management functionality. Users can upload docx, pdf, txt, and md format documents and generate high-quality knowledge base question-answer pairs by invoking large models through the 'Parse Document' feature.

PDFMathTranslate is a tool designed for translating scientific papers and conducting bilingual comparisons. It preserves formulas, charts, table of contents, and annotations. The tool supports multiple languages and diverse translation services. It provides a command-line tool, interactive user interface, and Docker deployment. Users can try the application through online demos. The tool offers various installation methods including command-line, portable, graphic user interface, and Docker. Advanced options allow users to customize translation settings. Additionally, the tool supports secondary development through APIs for Python and HTTP. Future plans include parsing layout with DocLayNet based models, fixing page rotation and format issues, supporting non-PDF/A files, and integrating plugins for Zotero and Obsidian.

The grps-trtllm repository is a C++ implementation of a high-performance OpenAI LLM service, combining GRPS and TensorRT-LLM. It supports functionalities like Chat, Ai-agent, and Multi-modal. The repository offers advantages over triton-trtllm, including a complete LLM service implemented in pure C++, integrated tokenizer supporting huggingface and sentencepiece, custom HTTP functionality for OpenAI interface, support for different LLM prompt styles and result parsing styles, integration with tensorrt backend and opencv library for multi-modal LLM, and stable performance improvement compared to triton-trtllm.

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.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Nvidia GPU exporter for prometheus, using `nvidia-smi` binary to gather metrics.

Tracecat is an open-source automation platform for security teams. It's designed to be simple but powerful, with a focus on AI features and a practitioner-obsessed UI/UX. Tracecat can be used to automate a variety of tasks, including phishing email investigation, evidence collection, and remediation plan generation.

OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.