robustmq

RobustMQ is a next-generation, high-performance, multi-protocol message queue built in Rust. It aims to create a unified messaging infrastructure tailored for modern cloud-native and AI systems. With features like high performance, distributed architecture, multi-protocol support, pluggable storage, cloud-native readiness, multi-tenancy, security features, observability, and user-friendliness, RobustMQ is designed to be production-ready and become a top-level Apache project in the message queue ecosystem by the second half of 2025.

CB-Tumblebug (CB-TB) is a system for managing multi-cloud infrastructure consisting of resources from multiple cloud service providers. It provides an overview, features, and architecture. The tool supports various cloud providers and resource types, with ongoing development and localization efforts. Users can deploy a multi-cloud infra with GPUs, enjoy multiple LLMs in parallel, and utilize LLM-related scripts. The tool requires Linux, Docker, Docker Compose, and Golang for building the source. Users can run CB-TB with Docker Compose or from the Makefile, set up prerequisites, contribute to the project, and view a list of contributors. The tool is licensed under an open-source license.

Cedar OS is an open-source framework that bridges the gap between AI agents and React applications, enabling the creation of AI-native applications where agents can interact with the application state like users. It focuses on providing intuitive and powerful ways for humans to interact with AI through features like full state integration, real-time streaming, voice-first design, and flexible architecture. Cedar OS offers production-ready chat components, agentic state management, context-aware mentions, voice integration, spells & quick actions, and fully customizable UI. It differentiates itself by offering a true AI-native architecture, developer-first experience, production-ready features, and extensibility. Built with TypeScript support, Cedar OS is designed for developers working on ambitious AI-native applications.

Youtu-GraphRAG is a vertically unified agentic paradigm that connects the entire framework based on graph schema, allowing seamless domain transfer with minimal intervention. It introduces key innovations like schema-guided hierarchical knowledge tree construction, dually-perceived community detection, agentic retrieval, advanced construction and reasoning capabilities, fair anonymous dataset 'AnonyRAG', and unified configuration management. The framework demonstrates robustness with lower token cost and higher accuracy compared to state-of-the-art methods, enabling enterprise-scale deployment with minimal manual intervention for new domains.

GraphBit is an industry-grade agentic AI framework built for developers and AI teams that demand stability, scalability, and low resource usage. It is written in Rust for maximum performance and safety, delivering significantly lower CPU usage and memory footprint compared to leading alternatives. The framework is designed to run multi-agent workflows in parallel, persist memory across steps, recover from failures, and ensure 100% task success under load. With lightweight architecture, observability, and concurrency support, GraphBit is suitable for deployment in high-scale enterprise environments and low-resource edge scenarios.

reDB Node is a distributed, policy-driven data mesh platform that enables True Data Portability across various databases, warehouses, clouds, and environments. It unifies data access, data mobility, and schema transformation into one open platform. Built for developers, architects, and AI systems, reDB addresses the challenges of fragmented data ecosystems in modern enterprises by providing multi-database interoperability, automated schema versioning, zero-downtime migration, real-time developer data environments with obfuscation, quantum-resistant encryption, and policy-based access control. The project aims to build a foundation for future-proof data infrastructure.

PaddleNLP is an easy-to-use and high-performance NLP library. It aggregates high-quality pre-trained models in the industry and provides out-of-the-box development experience, covering a model library for multiple NLP scenarios with industry practice examples to meet developers' flexible customization needs.

xLLM is an efficient LLM inference framework optimized for Chinese AI accelerators, enabling enterprise-grade deployment with enhanced efficiency and reduced cost. It adopts a service-engine decoupled inference architecture, achieving breakthrough efficiency through technologies like elastic scheduling, dynamic PD disaggregation, multi-stream parallel computing, graph fusion optimization, and global KV cache management. xLLM supports deployment of mainstream large models on Chinese AI accelerators, empowering enterprises in scenarios like intelligent customer service, risk control, supply chain optimization, ad recommendation, and more.

LangGraph.js is a library for building stateful, multi-actor applications with LLMs, offering benefits such as cycles, controllability, and persistence. It allows defining flows involving cycles, providing fine-grained control over application flow and state. Inspired by Pregel and Apache Beam, it includes features like loops, persistence, human-in-the-loop workflows, and streaming support. LangGraph integrates seamlessly with LangChain.js and LangSmith but can be used independently.

LMForge is an end-to-end LLMOps platform designed for multi-model agents. It provides a comprehensive solution for managing and deploying large language models efficiently. The platform offers tools for training, fine-tuning, and deploying various types of language models, enabling users to streamline the development and deployment process. With LMForge, users can easily experiment with different model architectures, optimize hyperparameters, and scale their models to meet specific requirements. The platform also includes features for monitoring model performance, managing datasets, and collaborating with team members, making it a versatile tool for researchers and developers working with language models.

This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.

AIaW is a next-generation LLM client with full functionality, lightweight, and extensible. It supports various basic functions such as streaming transfer, image uploading, and latex formulas. The tool is cross-platform with a responsive interface design. It supports multiple service providers like OpenAI, Anthropic, and Google. Users can modify questions, regenerate in a forked manner, and visualize conversations in a tree structure. Additionally, it offers features like file parsing, video parsing, plugin system, assistant market, local storage with real-time cloud sync, and customizable interface themes. Users can create multiple workspaces, use dynamic prompt word variables, extend plugins, and benefit from detailed design elements like real-time content preview, optimized code pasting, and support for various file types.

Agentic is a lightweight and flexible Python library for building multi-agent systems. It provides a simple and intuitive API for creating and managing agents, defining their behaviors, and simulating interactions in a multi-agent environment. With Agentic, users can easily design and implement complex agent-based models to study emergent behaviors, social dynamics, and decentralized decision-making processes. The library supports various agent architectures, communication protocols, and simulation scenarios, making it suitable for a wide range of research and educational applications in the fields of artificial intelligence, machine learning, social sciences, and robotics.

Spec Workflow MCP is a Model Context Protocol (MCP) server that offers structured spec-driven development workflow tools for AI-assisted software development. It includes a real-time web dashboard and a VSCode extension for monitoring and managing project progress directly in the development environment. The tool supports sequential spec creation, real-time monitoring of specs and tasks, document management, archive system, task progress tracking, approval workflow, bug reporting, template system, and works on Windows, macOS, and Linux.

Alphadec is a timezone-agnostic, human-readable time format designed for global timestamp synchronization. It encodes any UTC timestamp into a structured string that is lexically sortable, time-series friendly, human-readable, and compact. Alphadec serves as a powerful primitive for AI models to understand the passage of time and offers emergent properties like rhythmic timestamps, chronological ID fragments, and version labels. It is AI-friendly, database-friendly, and collision-free with ISO time formats. Alphadec's canonical format consists of parts representing the UTC year, periods, arcs, bars, beats, and milliseconds offset, allowing for efficient time-range queries and index stability.

Kohaku-NAI is a simple Novel-AI client with utilities like a generation server, saving images automatically, account pool, and an auth system. It also includes a standalone client, a DC bot based on the generation server, and a stable-diffusion-webui extension. Users can use it to generate images with NAI API within sd-webui, as a standalone client, gen server, or DC bot. The project aims to add features like QoS system, better client, random prompts, and fetch account info in the future.

RobustMQ is a next-generation, high-performance, multi-protocol message queue built in Rust. It aims to create a unified messaging infrastructure tailored for modern cloud-native and AI systems. With features like high performance, distributed architecture, multi-protocol support, pluggable storage, cloud-native readiness, multi-tenancy, security features, observability, and user-friendliness, RobustMQ is designed to be production-ready and become a top-level Apache project in the message queue ecosystem by the second half of 2025.

AirGo is a front and rear end separation, multi user, multi protocol proxy service management system, simple and easy to use. It supports vless, vmess, shadowsocks, and hysteria2.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.