Shell-AI (`shai`) is a CLI utility that enables users to input commands in natural language and receive single-line command suggestions. It leverages natural language understanding and interactive CLI tools to enhance command line interactions. Users can describe tasks in plain English and receive corresponding command suggestions, making it easier to execute commands efficiently. Shell-AI supports cross-platform usage and is compatible with Azure OpenAI deployments, offering a user-friendly and efficient way to interact with the command line.

DeepSearcher is a tool that combines reasoning LLMs and Vector Databases to perform search, evaluation, and reasoning based on private data. It is suitable for enterprise knowledge management, intelligent Q&A systems, and information retrieval scenarios. The tool maximizes the utilization of enterprise internal data while ensuring data security, supports multiple embedding models, and provides support for multiple LLMs for intelligent Q&A and content generation. It also includes features like private data search, vector database management, and document loading with web crawling capabilities under development.

AIAvatarKit is a tool for building AI-based conversational avatars quickly. It supports various platforms like VRChat and cluster, along with real-world devices. The tool is extensible, allowing unlimited capabilities based on user needs. It requires VOICEVOX API, Google or Azure Speech Services API keys, and Python 3.10. Users can start conversations out of the box and enjoy seamless interactions with the avatars.

Promptic is a tool designed for LLM app development, providing a productive and pythonic way to build LLM applications. It leverages LiteLLM, allowing flexibility to switch LLM providers easily. Promptic focuses on building features by providing type-safe structured outputs, easy-to-build agents, streaming support, automatic prompt caching, and built-in conversation memory.

HF-Waitress is a powerful server application for deploying and interacting with HuggingFace Transformer models. It simplifies running open-source Large Language Models (LLMs) locally on-device, providing on-the-fly quantization via BitsAndBytes, HQQ, and Quanto. It requires no manual model downloads, offers concurrency, streaming responses, and supports various hardware and platforms. The server uses a `config.json` file for easy configuration management and provides detailed error handling and logging.

Scylla is an intelligent proxy pool tool designed for humanities, enabling users to extract content from the internet and build their own Large Language Models in the AI era. It features automatic proxy IP crawling and validation, an easy-to-use JSON API, a simple web-based user interface, HTTP forward proxy server, Scrapy and requests integration, and headless browser crawling. Users can start using Scylla with just one command, making it a versatile tool for various web scraping and content extraction tasks.

OpenEdison is a secure MCP control panel that connects AI to data/software with additional security controls to reduce data exfiltration risks. It helps address the lethal trifecta problem by providing visibility, monitoring potential threats, and alerting on data interactions. The tool offers features like data leak monitoring, controlled execution, easy configuration, visibility into agent interactions, a simple API, and Docker support. It integrates with LangGraph, LangChain, and plain Python agents for observability and policy enforcement. OpenEdison helps gain observability, control, and policy enforcement for AI interactions with systems of records, existing company software, and data to reduce risks of AI-caused data leakage.

Crush is a versatile tool designed to enhance coding workflows in your terminal. It offers support for multiple LLMs, allows for flexible switching between models, and enables session-based work management. Crush is extensible through MCPs and works across various operating systems. It can be installed using package managers like Homebrew and NPM, or downloaded directly. Crush supports various APIs like Anthropic, OpenAI, Groq, and Google Gemini, and allows for customization through environment variables. The tool can be configured locally or globally, and supports LSPs for additional context. Crush also provides options for ignoring files, allowing tools, and configuring local models. It respects `.gitignore` files and offers logging capabilities for troubleshooting and debugging.

Gmail AutoAuth MCP Server is a Model Context Protocol (MCP) server designed for Gmail integration in Claude Desktop. It supports auto authentication and enables AI assistants to manage Gmail through natural language interactions. The server provides comprehensive features for sending emails, reading messages, managing labels, searching emails, and batch operations. It offers full support for international characters, email attachments, and Gmail API integration. Users can install and authenticate the server via Smithery or manually with Google Cloud Project credentials. The server supports both Desktop and Web application credentials, with global credential storage for convenience. It also includes Docker support and instructions for cloud server authentication.

Use the OpenAI API with Ruby! 🤖🩵 Stream text with GPT-4, transcribe and translate audio with Whisper, or create images with DALL·E... Hire me | 🎮 Ruby AI Builders Discord | 🐦 Twitter | 🧠 Anthropic Gem | 🚂 Midjourney Gem ## Table of Contents * Ruby OpenAI * Table of Contents * Installation * Bundler * Gem install * Usage * Quickstart * With Config * Custom timeout or base URI * Extra Headers per Client * Logging * Errors * Faraday middleware * Azure * Ollama * Counting Tokens * Models * Examples * Chat * Streaming Chat * Vision * JSON Mode * Functions * Edits * Embeddings * Batches * Files * Finetunes * Assistants * Threads and Messages * Runs * Runs involving function tools * Image Generation * DALL·E 2 * DALL·E 3 * Image Edit * Image Variations * Moderations * Whisper * Translate * Transcribe * Speech * Errors * Development * Release * Contributing * License * Code of Conduct

The Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis. It integrates seamlessly with MCP (Model Content Protocol) clients, enabling AI-driven workflows to interact with structured and unstructured data in Redis. The server supports natural language queries, seamless MCP integration, full Redis support for various data types, search and filtering capabilities, scalability, and lightweight design. It provides tools for managing data stored in Redis, such as string, hash, list, set, sorted set, pub/sub, streams, JSON, query engine, and server management. Installation can be done from PyPI or GitHub, with options for testing, development, and Docker deployment. Configuration can be via command line arguments or environment variables. Integrations include OpenAI Agents SDK, Augment, Claude Desktop, and VS Code with GitHub Copilot. Use cases include AI assistants, chatbots, data search & analytics, and event processing. Contributions are welcome under the MIT License.

vim-ai is a plugin that adds Artificial Intelligence (AI) capabilities to Vim and Neovim. It allows users to generate code, edit text, and have interactive conversations with GPT models powered by OpenAI's API. The plugin uses OpenAI's API to generate responses, requiring users to set up an account and obtain an API key. It supports various commands for text generation, editing, and chat interactions, providing a seamless integration of AI features into the Vim text editor environment.

Jupyter MCP Server is a Model Context Protocol (MCP) server implementation that enables real-time interaction with Jupyter Notebooks. It allows AI to edit, document, and execute code for data analysis and visualization. The server offers features like real-time control, smart execution, and MCP compatibility. Users can use tools such as insert_execute_code_cell, append_markdown_cell, get_notebook_info, and read_cell for advanced interactions with Jupyter notebooks.

Semgrep MCP Server is a beta server under active development for using Semgrep to scan code for security vulnerabilities. It provides a Model Context Protocol (MCP) for various coding tools to get specialized help in tasks. Users can connect to Semgrep AppSec Platform, scan code for vulnerabilities, customize Semgrep rules, analyze and filter scan results, and compare results. The tool is published on PyPI as semgrep-mcp and can be installed using pip, pipx, uv, poetry, or other methods. It supports CLI and Docker environments for running the server. Integration with VS Code is also available for quick installation. The project welcomes contributions and is inspired by core technologies like Semgrep and MCP, as well as related community projects and tools.

Firecrawl MCP Server is a Model Context Protocol (MCP) server implementation that integrates with Firecrawl for web scraping capabilities. It supports features like scrape, crawl, search, extract, and batch scrape. It provides web scraping with JS rendering, URL discovery, web search with content extraction, automatic retries with exponential backoff, credit usage monitoring, comprehensive logging system, support for cloud and self-hosted FireCrawl instances, mobile/desktop viewport support, and smart content filtering with tag inclusion/exclusion. The server includes configurable parameters for retry behavior and credit usage monitoring, rate limiting and batch processing capabilities, and tools for scraping, batch scraping, checking batch status, searching, crawling, and extracting structured information from web pages.

Firecrawl MCP Server is a Model Context Protocol (MCP) server implementation that integrates with Firecrawl for web scraping capabilities. It offers features such as web scraping, crawling, and discovery, search and content extraction, deep research and batch scraping, automatic retries and rate limiting, cloud and self-hosted support, and SSE support. The server can be configured to run with various tools like Cursor, Windsurf, SSE Local Mode, Smithery, and VS Code. It supports environment variables for cloud API and optional configurations for retry settings and credit usage monitoring. The server includes tools for scraping, batch scraping, mapping, searching, crawling, and extracting structured data from web pages. It provides detailed logging and error handling functionalities for robust performance.

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.

Danswer is an open-source Gen-AI Chat and Unified Search tool that connects to your company's docs, apps, and people. It provides a Chat interface and plugs into any LLM of your choice. Danswer can be deployed anywhere and for any scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your own control. Danswer is MIT licensed and designed to be modular and easily extensible. The system also comes fully ready for production usage with user authentication, role management (admin/basic users), chat persistence, and a UI for configuring Personas (AI Assistants) and their Prompts. Danswer also serves as a Unified Search across all common workplace tools such as Slack, Google Drive, Confluence, etc. By combining LLMs and team specific knowledge, Danswer becomes a subject matter expert for the team. Imagine ChatGPT if it had access to your team's unique knowledge! It enables questions such as "A customer wants feature X, is this already supported?" or "Where's the pull request for feature Y?"

Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel _special_ , however, is its ability to _automatically_ orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

Floneum is a graph editor that makes it easy to develop your own AI workflows. It uses large language models (LLMs) to run AI models locally, without any external dependencies or even a GPU. This makes it easy to use LLMs with your own data, without worrying about privacy. Floneum also has a plugin system that allows you to improve the performance of LLMs and make them work better for your specific use case. Plugins can be used in any language that supports web assembly, and they can control the output of LLMs with a process similar to JSONformer or guidance.

MindsDB is a platform for customizing AI from enterprise data. You can create, serve, and fine-tune models in real-time from your database, vector store, and application data. MindsDB "enhances" SQL syntax with AI capabilities to make it accessible for developers worldwide. With MindsDB’s nearly 200 integrations, any developer can create AI customized for their purpose, faster and more securely. Their AI systems will constantly improve themselves — using companies’ own data, in real-time.

AiScript is a lightweight scripting language that runs on JavaScript. It supports arrays, objects, and functions as first-class citizens, and is easy to write without the need for semicolons or commas. AiScript runs in a secure sandbox environment, preventing infinite loops from freezing the host. It also allows for easy provision of variables and functions from the host.

Activepieces is an open source replacement for Zapier, designed to be extensible through a type-safe pieces framework written in Typescript. It features a user-friendly Workflow Builder with support for Branches, Loops, and Drag and Drop. Activepieces integrates with Google Sheets, OpenAI, Discord, and RSS, along with 80+ other integrations. The list of supported integrations continues to grow rapidly, thanks to valuable contributions from the community. Activepieces is an open ecosystem; all piece source code is available in the repository, and they are versioned and published directly to npmjs.com upon contributions. If you cannot find a specific piece on the pieces roadmap, please submit a request by visiting the following link: Request Piece Alternatively, if you are a developer, you can quickly build your own piece using our TypeScript framework. For guidance, please refer to the following guide: Contributor's Guide

Superagent is an open source framework that enables any developer to integrate production ready AI Assistants into any application in a matter of minutes.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

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

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.

AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.

This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).

The AWS Generative AI Constructs Library is an open-source extension of the AWS Cloud Development Kit (AWS CDK) that provides multi-service, well-architected patterns for quickly defining solutions in code to create predictable and repeatable infrastructure, called constructs. The goal of AWS Generative AI CDK Constructs is to help developers build generative AI solutions using pattern-based definitions for their architecture. The patterns defined in AWS Generative AI CDK Constructs are high level, multi-service abstractions of AWS CDK constructs that have default configurations based on well-architected best practices. The library is organized into logical modules using object-oriented techniques to create each architectural pattern model.

OpenVINO™ Model Server (OVMS) is a high-performance system for serving models. Implemented in C++ for scalability and optimized for deployment on Intel architectures, the model server uses the same architecture and API as TensorFlow Serving and KServe while applying OpenVINO for inference execution. Inference service is provided via gRPC or REST API, making deploying new algorithms and AI experiments easy.

Deploy langgenius/dify, an LLM based chat bot app on kubernetes with helm chart.