Dubbo Kubernetes provides support for building and deploying Dubbo applications in various environments, including Kubernetes and Alibaba Cloud ACK. It includes dubboctl for command line utility, dubbod for the control plane built on Istio, navigator for configuring proxies at runtime, and operator for user-friendly options to operate the dubbo proxyless mesh.

ToolHive is a tool designed to simplify and secure Model Context Protocol (MCP) servers. It allows users to easily discover, deploy, and manage MCP servers by launching them in isolated containers with minimal setup and security concerns. The tool offers instant deployment, secure default settings, compatibility with Docker and Kubernetes, seamless integration with popular clients, and availability as a GUI desktop app, CLI, and Kubernetes Operator.

DeepFlow is an open-source project that provides deep observability for complex cloud-native and AI applications. It offers Zero Code data collection with eBPF for metrics, distributed tracing, request logs, and function profiling. DeepFlow is integrated with SmartEncoding to achieve Full Stack correlation and efficient access to all observability data. With DeepFlow, cloud-native and AI applications automatically gain deep observability, removing the burden of developers continually instrumenting code and providing monitoring and diagnostic capabilities covering everything from code to infrastructure for DevOps/SRE teams.

Agent Development Kit (ADK) is an open-source, code-first toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control. It is a flexible and modular framework optimized for Gemini and the Google ecosystem, model-agnostic, deployment-agnostic, and compatible with other frameworks. ADK simplifies agent development by making it feel more like software development, enabling developers to create, deploy, and orchestrate agentic architectures from simple tasks to complex workflows.

Higress is an open-source cloud-native API gateway built on the core of Istio and Envoy, based on Alibaba's internal practice of Envoy Gateway. It is designed for AI-native API gateway, serving AI businesses such as Tongyi Qianwen APP, Bailian Big Model API, and Machine Learning PAI platform. Higress provides capabilities to interface with LLM model vendors, AI observability, multi-model load balancing/fallback, AI token flow control, and AI caching. It offers features for AI gateway, Kubernetes Ingress gateway, microservices gateway, and security protection gateway, with advantages in production-level scalability, stream processing, extensibility, and ease of use.

1Panel is an open-source, modern web-based control panel for Linux server management. It provides efficient management through a user-friendly web graphical interface, enabling users to effortlessly manage their Linux servers. Key features include host monitoring, file management, database administration, container management, rapid website deployment with WordPress integration, an application store for easy installation and updates, security and reliability through containerization and secure application deployment practices, integrated firewall management, log auditing capabilities, and one-click backup & restore functionality supporting various cloud storage solutions.

ApeRAG is a production-ready platform for Retrieval-Augmented Generation (RAG) that combines Graph RAG, vector search, and full-text search with advanced AI agents. It is ideal for building Knowledge Graphs, Context Engineering, and deploying intelligent AI agents for autonomous search and reasoning across knowledge bases. The platform offers features like advanced index types, intelligent AI agents with MCP support, enhanced Graph RAG with entity normalization, multimodal processing, hybrid retrieval engine, MinerU integration for document parsing, production-grade deployment with Kubernetes, enterprise management features, MCP integration, and developer-friendly tools for customization and contribution.

Kortix is an open-source platform designed to build, manage, and train AI agents for various tasks. It allows users to create autonomous agents, from general-purpose assistants to specialized automation tools. The platform offers capabilities such as browser automation, file management, web intelligence, system operations, API integrations, and agent building tools. Users can create custom agents tailored to specific domains, workflows, or business needs, enabling tasks like research & analysis, browser automation, file & document management, data processing & analysis, and system administration.

ClearML is a suite of tools designed to streamline the machine learning workflow. It includes an experiment manager, MLOps/LLMOps, data management, and model serving capabilities. ClearML is open-source and offers a free tier hosting option. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm. ClearML provides extensive logging capabilities, including source control info, execution environment, hyper-parameters, and experiment outputs. It also offers automation features, such as remote job execution and pipeline creation. ClearML is designed to be easy to integrate, requiring only two lines of code to add to existing scripts. It aims to improve collaboration, visibility, and data transparency within ML teams.

WeKnora is a document understanding and semantic retrieval framework based on large language models (LLM), designed specifically for scenarios with complex structures and heterogeneous content. The framework adopts a modular architecture, integrating multimodal preprocessing, semantic vector indexing, intelligent recall, and large model generation reasoning to build an efficient and controllable document question-answering process. The core retrieval process is based on the RAG (Retrieval-Augmented Generation) mechanism, combining context-relevant segments with language models to achieve higher-quality semantic answers. It supports various document formats, intelligent inference, flexible extension, efficient retrieval, ease of use, and security and control. Suitable for enterprise knowledge management, scientific literature analysis, product technical support, legal compliance review, and medical knowledge assistance.

OpenOps is a No-Code FinOps automation platform designed to help organizations reduce cloud costs and streamline financial operations. It offers customizable workflows for automating key FinOps processes, comes with its own Excel-like database and visualization system, and enables collaboration between different teams. OpenOps integrates seamlessly with major cloud providers, third-party FinOps tools, communication platforms, and project management tools, providing a comprehensive solution for efficient cost-saving measures implementation.

Docq is a private and secure GenAI tool designed to extract knowledge from business documents, enabling users to find answers independently. It allows data to stay within organizational boundaries, supports self-hosting with various cloud vendors, and offers multi-model and multi-modal capabilities. Docq is extensible, open-source (AGPLv3), and provides commercial licensing options. The tool aims to be a turnkey solution for organizations to adopt AI innovation safely, with plans for future features like more data ingestion options and model fine-tuning.

Wanwu AI Agent Platform is an enterprise-grade one-stop commercially friendly AI agent development platform designed for business scenarios. It provides enterprises with a safe, efficient, and compliant one-stop AI solution. The platform integrates cutting-edge technologies such as large language models and business process automation to build an AI engineering platform covering model full life-cycle management, MCP, web search, AI agent rapid development, enterprise knowledge base construction, and complex workflow orchestration. It supports modular architecture design, flexible functional expansion, and secondary development, reducing the application threshold of AI technology while ensuring security and privacy protection of enterprise data. It accelerates digital transformation, cost reduction, efficiency improvement, and business innovation for enterprises of all sizes.

ClearML is an auto-magical suite of tools designed to streamline AI workflows. It includes modules for experiment management, MLOps/LLMOps, data management, model serving, and more. ClearML offers features like experiment tracking, model serving, orchestration, and automation. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm for remote debugging. ClearML aims to simplify collaboration, automate processes, and enhance visibility in AI projects.

Heurist Agent Framework is a flexible multi-interface AI agent framework that allows processing text and voice messages, generating images and videos, interacting across multiple platforms, fetching and storing information in a knowledge base, accessing external APIs and tools, and composing complex workflows using Mesh Agents. It supports various platforms like Telegram, Discord, Twitter, Farcaster, REST API, and MCP. The framework is built on a modular architecture and provides core components, tools, workflows, and tool integration with MCP support.

AIGNE Framework is a functional AI application development framework designed to simplify and accelerate the process of building modern applications. It combines functional programming features, powerful artificial intelligence capabilities, and modular design principles to help developers easily create scalable solutions. With key features like modular design, TypeScript support, multiple AI model support, flexible workflow patterns, MCP protocol integration, code execution capabilities, and Blocklet ecosystem integration, AIGNE Framework offers a comprehensive solution for developers. The framework provides various workflow patterns such as Workflow Router, Workflow Sequential, Workflow Concurrency, Workflow Handoff, Workflow Reflection, Workflow Orchestration, Workflow Code Execution, and Workflow Group Chat to address different application scenarios efficiently. It also includes built-in MCP support for running MCP servers and integrating with external MCP servers, along with packages for core functionality, agent library, CLI, and various models like OpenAI, Gemini, Claude, and Nova.

This repository contains Jupyter-based tutorials for NLP, ML, AI in Python for classes in Computational Linguistics, Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) at Indiana University.

Open Parse is a Python library for visually discerning document layouts and chunking them effectively. It is designed to fill the gap in open-source libraries for handling complex documents. Unlike text splitting, which converts a file to raw text and slices it up, Open Parse visually analyzes documents for superior LLM input. It also supports basic markdown for parsing headings, bold, and italics, and has high-precision table support, extracting tables into clean Markdown formats with accuracy that surpasses traditional tools. Open Parse is extensible, allowing users to easily implement their own post-processing steps. It is also intuitive, with great editor support and completion everywhere, making it easy to use and learn.

The MoonshotAI-Cookbook provides example code and guides for accomplishing common tasks with the MoonshotAI API. To run these examples, you'll need an MoonshotAI account and associated API key. Most code examples are written in Python, though the concepts can be applied in any language.

This repository is dedicated to the learning and exchange of resources for the School of Artificial Intelligence at Anhui University. Notes will be published on this website first: https://www.aoaoaoao.cn and will be synchronized to the repository regularly. You can also contact me at [email protected].

This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.

This repository is for the book 'Building AI Applications with ChatGPT APIs' published by Packt. It provides code examples and instructions for mastering ChatGPT, Whisper, and DALL-E APIs through building innovative AI projects. Readers will learn to develop AI applications using ChatGPT APIs, integrate them with frameworks like Flask and Django, create AI-generated art with DALL-E APIs, and optimize ChatGPT models through fine-tuning.

This repository contains a collection of sample applications and Jupyter Notebooks for hands-on experience with Pinecone vector databases and common AI patterns, tools, and algorithms. It includes production-ready examples for review and support, as well as learning-optimized examples for exploring AI techniques and building applications. Users can contribute, provide feedback, and collaborate to improve the resource.

LinGoose is a modular Go framework designed for building AI/LLM applications. It offers the flexibility to import only the necessary modules, abstracts features for customization, and provides a comprehensive solution for developing AI/LLM applications from scratch. The framework simplifies the process of creating intelligent applications by allowing users to choose preferred implementations or create their own. LinGoose empowers developers to leverage its capabilities to streamline the development of cutting-edge AI and LLM projects.

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.