Sparka AI is a multi-provider AI chat tool that allows users to access various AI models like Claude, GPT-5, Gemini, and Grok through a single interface. It offers features such as document analysis, image generation, code execution, and research tools without the need for multiple subscriptions. The tool is open-source, production-ready, and provides capabilities for collaboration, secure authentication, attachment support, AI-powered image generation, syntax highlighting, resumable streams, chat branching, chat sharing, deep research, code execution, document creation, and web analytics. Built with modern technologies for scalability and performance, Sparka AI integrates with Vercel AI SDK, tRPC, Drizzle ORM, PostgreSQL, Redis, and AI SDK Gateway.

KitchenAI is an open-source toolkit designed to simplify AI development by serving as an AI backend and LLMOps solution. It aims to empower developers to focus on delivering results without being bogged down by AI infrastructure complexities. With features like simplifying AI integration, providing an AI backend, and empowering developers, KitchenAI streamlines the process of turning AI experiments into production-ready APIs. It offers built-in LLMOps features, is framework-agnostic and extensible, and enables faster time-to-production. KitchenAI is suitable for application developers, AI developers & data scientists, and platform & infra engineers, allowing them to seamlessly integrate AI into apps, deploy custom AI techniques, and optimize AI services with a modular framework. The toolkit eliminates the need to build APIs and infrastructure from scratch, making it easier to deploy AI code as production-ready APIs in minutes. KitchenAI also provides observability, tracing, and evaluation tools, and offers a Docker-first deployment approach for scalability and confidence.

LLMChat is an all-in-one AI chat interface that supports multiple language models, offers a plugin library for enhanced functionality, enables web search capabilities, allows customization of AI assistants, provides text-to-speech conversion, ensures secure local data storage, and facilitates data import/export. It also includes features like knowledge spaces, prompt library, personalization, and can be installed as a Progressive Web App (PWA). The tech stack includes Next.js, TypeScript, Pglite, LangChain, Zustand, React Query, Supabase, Tailwind CSS, Framer Motion, Shadcn, and Tiptap. The roadmap includes upcoming features like speech-to-text and knowledge spaces.

Zettelgarden is a human-centric, open-source personal knowledge management system that helps users develop and maintain their understanding of the world. It focuses on creating and connecting atomic notes, thoughtful AI integration, and scalability from personal notes to company knowledge bases. The project is actively evolving, with features subject to change based on community feedback and development priorities.

AIGNE Hub is a unified AI gateway that manages connections to multiple LLM and AIGC providers, eliminating the complexity of handling API keys, usage tracking, and billing across different AI services. It provides self-hosting capabilities, multi-provider management, unified security, usage analytics, flexible billing, and seamless integration with the AIGNE framework. The tool supports various AI providers and deployment scenarios, catering to both enterprise self-hosting and service provider modes. Users can easily deploy and configure AI providers, enable billing, and utilize core capabilities such as chat completions, image generation, embeddings, and RESTful APIs. AIGNE Hub ensures secure access, encrypted API key management, user permissions, and audit logging. Built with modern technologies like AIGNE Framework, Node.js, TypeScript, React, SQLite, and Blocklet for cloud-native deployment.

The Eureka Framework is an open-source toolkit that leverages advanced Artificial Intelligence and Decentralized Science principles to revolutionize scientific discovery. It enables researchers, developers, and decentralized organizations to explore scientific papers, conduct AI-driven experiments, monetize research contributions, provide token-gated access to AI agents, and customize AI agents for specific research domains. The framework also offers features like a RESTful API, robust scheduler for task automation, and webhooks for real-time notifications, empowering users to automate research tasks, enhance productivity, and foster a committed research community.

Open-WebUI-Functions is a collection of Python-based functions that extend Open WebUI with custom pipelines, filters, and integrations. Users can interact with AI models, process data efficiently, and customize the Open WebUI experience. It includes features like custom pipelines, data processing filters, Azure AI support, N8N workflow integration, flexible configuration, secure API key management, and support for both streaming and non-streaming processing. The functions require an active Open WebUI instance, may need external AI services like Azure AI, and admin access for installation. Security features include automatic encryption of sensitive information like API keys. Pipelines include Azure AI Foundry, N8N, Infomaniak, and Google Gemini. Filters like Time Token Tracker measure response time and token usage. Integrations with Azure AI, N8N, Infomaniak, and Google are supported. Contributions are welcome, and the project is licensed under Apache License 2.0.

Official SDKs for the ScrapeGraph AI API - Intelligent web scraping and search powered by AI. Extract structured data from any webpage or perform AI-powered web searches with natural language prompts. The SDK offers features such as SmartScraper for data extraction, SearchScraper for AI-powered web search, Markdownify for converting webpages to markdown, SmartCrawler for intelligent crawling, AgenticScraper for automated browser actions, and more. It provides seamless integration with popular frameworks and tools, supports Python and JavaScript SDKs, LLM frameworks, low-code platforms, and offers core features like AI-powered extraction, structured output, multiple data formats, high performance, and enterprise-grade security.

Monadic Chat is a locally hosted web application designed to create and utilize intelligent chatbots. It provides a Linux environment on Docker to GPT and other LLMs, enabling the execution of advanced tasks that require external tools. The tool supports voice interaction, image and video recognition and generation, and AI-to-AI chat, making it useful for using AI and developing various applications. It is available for Mac, Windows, and Linux (Debian/Ubuntu) with easy-to-use installers.

DSPy.rb is a Ruby framework for building reliable LLM applications using composable, type-safe modules. It enables developers to define typed signatures and compose them into pipelines, offering a more structured approach compared to traditional prompting. The framework embraces Ruby conventions and adds innovations like CodeAct agents and enhanced production instrumentation, resulting in scalable LLM applications that are robust and efficient. DSPy.rb is actively developed, with a focus on stability and real-world feedback through the 0.x series before reaching a stable v1.0 API.

This repository contains a significant portion of the FULL official v0, Manus, and Cursor system prompts and AI models. It includes over 5,000+ lines of insights into their structure and functionality. The available files include FULL v0, v0 model.txt, v0 tools.txt, Cursor (with cursor agent.txt, cursor ask.txt, cursor edit.txt), and Manus Folder with multiple files inside.

Lidar AI Solution is a highly optimized repository for self-driving 3D lidar, providing solutions for sparse convolution, BEVFusion, CenterPoint, OSD, and Conversion. It includes CUDA and TensorRT implementations for various tasks such as 3D sparse convolution, BEVFusion, CenterPoint, PointPillars, V2XFusion, cuOSD, cuPCL, and YUV to RGB conversion. The repository offers easy-to-use solutions, high accuracy, low memory usage, and quantization options for different tasks related to self-driving technology.

Agent Hub is a platform for AI Agent solutions, containing three different projects aimed at transforming enterprise workflows, enhancing personalized language learning experiences, and enriching multimodal interactions. The projects include GitHub Sentinel for project management and automatic updates, LanguageMentor for personalized language learning support, and ChatPPT for multimodal AI-driven insights and PowerPoint automation in enterprise settings. The future vision of agent-hub is to serve as a launchpad for more AI Agents catering to different industries and pushing the boundaries of AI technology. Users are encouraged to explore, clone the repository, and contribute to the development of transformative AI agents.

AI Coding Assistant is a free and open-source IntelliJ plugin that leverages cutting-edge Language Model APIs to enhance developers' coding experience. It seamlessly integrates with various leading LLM APIs, offers an intuitive toolbar UI, and allows granular control over API requests. With features like Code & Patch Chat, Planning with AI Agents, Markdown visualization, and versatile text processing capabilities, this tool aims to streamline coding workflows and boost productivity.

OS Chat is a free, open-source AI personal assistant that combines 40+ language models with powerful automation capabilities. It allows users to deploy background agents, connect services like Gmail, Calendar, Notion, GitHub, and Slack, and get things done through natural conversation. With features like smart automation, service connectors, AI models, chat management, interface customization, and premium features, OS Chat offers a comprehensive solution for managing digital life and workflows. It prioritizes privacy by being open source and self-hostable, with encrypted API key storage.

DeepNude AI List is a compilation of various NSFW AI tools that are designed for generating nude or suggestive content. The list includes tools like Dreampaint.net, Nudify.me, NoDress.io, Undress Her, and more. These tools utilize artificial intelligence algorithms to manipulate images and create provocative visuals. Users should exercise caution and responsibility when using such tools, as they may raise ethical and privacy concerns.

The Google Cloud Document AI Samples repository contains code samples and Community Samples demonstrating how to analyze, classify, and search documents using Google Cloud Document AI. It includes various projects showcasing different functionalities such as integrating with Google Drive, processing documents using Python, content moderation with Dialogflow CX, fraud detection, language extraction, paper summarization, tax processing pipeline, and more. The repository also provides access to test document files stored in a publicly-accessible Google Cloud Storage Bucket. Additionally, there are codelabs available for optical character recognition (OCR), form parsing, specialized processors, and managing Document AI processors. Community samples, like the PDF Annotator Sample, are also included. Contributions are welcome, and users can seek help or report issues through the repository's issues page. Please note that this repository is not an officially supported Google product and is intended for demonstrative purposes only.

The StepChat Free service provides high-speed streaming output, multi-turn dialogue support, online search support, long document interpretation, and image parsing. It offers zero-configuration deployment, multi-token support, and automatic session trace cleaning. It is fully compatible with the ChatGPT interface. Additionally, it provides seven other free APIs for various services. The repository includes a disclaimer about using reverse APIs and encourages users to avoid commercial use to prevent service pressure on the official platform. It offers online testing links, showcases different demos, and provides deployment guides for Docker, Docker-compose, Render, Vercel, and native deployments. The repository also includes information on using multiple accounts, optimizing Nginx reverse proxy, and checking the liveliness of refresh tokens.

The 'unilm' repository is a collection of tools, models, and architectures for Foundation Models and General AI, focusing on tasks such as NLP, MT, Speech, Document AI, and Multimodal AI. It includes various pre-trained models, such as UniLM, InfoXLM, DeltaLM, MiniLM, AdaLM, BEiT, LayoutLM, WavLM, VALL-E, and more, designed for tasks like language understanding, generation, translation, vision, speech, and multimodal processing. The repository also features toolkits like s2s-ft for sequence-to-sequence fine-tuning and Aggressive Decoding for efficient sequence-to-sequence decoding. Additionally, it offers applications like TrOCR for OCR, LayoutReader for reading order detection, and XLM-T for multilingual NMT.

searchGPT is an open-source project that aims to build a search engine based on Large Language Model (LLM) technology to provide natural language answers. It supports web search with real-time results, file content search, and semantic search from sources like the Internet. The tool integrates LLM technologies such as OpenAI and GooseAI, and offers an easy-to-use frontend user interface. The project is designed to provide grounded answers by referencing real-time factual information, addressing the limitations of LLM's training data. Contributions, especially from frontend developers, are welcome under the MIT License.

This repository contains tutorials for using Large Language Models (LLMs) in the U.S. Department of Defense. The tutorials utilize open-source frameworks and LLMs, allowing users to run them in their own cloud environments. The repository is maintained by the Defense Digital Service and welcomes contributions from users.

LARS is an application that enables users to run Large Language Models (LLMs) locally on their devices, upload their own documents, and engage in conversations where the LLM grounds its responses with the uploaded content. The application focuses on Retrieval Augmented Generation (RAG) to increase accuracy and reduce AI-generated inaccuracies. LARS provides advanced citations, supports various file formats, allows follow-up questions, provides full chat history, and offers customization options for LLM settings. Users can force enable or disable RAG, change system prompts, and tweak advanced LLM settings. The application also supports GPU-accelerated inferencing, multiple embedding models, and text extraction methods. LARS is open-source and aims to be the ultimate RAG-centric LLM application.

Eagle is a family of Vision-Centric High-Resolution Multimodal LLMs that enhance multimodal LLM perception using a mix of vision encoders and various input resolutions. The model features a channel-concatenation-based fusion for vision experts with different architectures and knowledge, supporting up to over 1K input resolution. It excels in resolution-sensitive tasks like optical character recognition and document understanding.

ERAG is an advanced system that combines lexical, semantic, text, and knowledge graph searches with conversation context to provide accurate and contextually relevant responses. This tool processes various document types, creates embeddings, builds knowledge graphs, and uses this information to answer user queries intelligently. It includes modules for interacting with web content, GitHub repositories, and performing exploratory data analysis using various language models.

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.