Clai is a command line context-feeder for AI tasks, supporting MCP client, vendor agnosticism, conversations, rate limit circumvention, profiles, and Unix-like functionality. Users can easily combine and tweak features for diverse use cases. Supported vendors include OpenAI, Anthropic, Mistral, Deepseek, Novita AI, Ollama, and Inception. Users need API keys for model access. Installation via 'go install' or setup script. 'clai help' provides guidance on usage. Glow can be installed for formatted markdown output.

Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models.

Diffusion Toolkit is an image metadata-indexer and viewer for AI-generated images. It helps you organize, search, and sort your ever-growing collection. Key features include: - Scanning images and storing prompts and other metadata (PNGInfo) - Searching for images using simple queries or filters - Viewing images and metadata easily - Tagging images with favorites, ratings, and NSFW flags - Sorting images by date created, aesthetic score, or rating - Auto-tagging NSFW images by keywords - Blurring images tagged as NSFW - Creating and managing albums - Viewing and searching prompts - Drag-and-drop functionality Diffusion Toolkit supports various image formats, including JPG/JPEG, PNG, WebP, and TXT metadata. It also supports metadata formats from popular AI image generators like AUTOMATIC1111, InvokeAI, NovelAI, Stable Diffusion, and more. You can use Diffusion Toolkit even on images without metadata and still enjoy features like rating and album management.

SimpleAICV_pytorch_training_examples is a repository that provides simple training and testing examples for various computer vision tasks such as image classification, object detection, semantic segmentation, instance segmentation, knowledge distillation, contrastive learning, masked image modeling, OCR text detection, OCR text recognition, human matting, salient object detection, interactive segmentation, image inpainting, and diffusion model tasks. The repository includes support for multiple datasets and networks, along with instructions on how to prepare datasets, train and test models, and use gradio demos. It also offers pretrained models and experiment records for download from huggingface or Baidu-Netdisk. The repository requires specific environments and package installations to run effectively.

This AI-powered git commit plugin for VSCode streamlines your commit and push processes, eliminating the need for manual confirmation. With a focus on minimizing keystrokes, the plugin leverages LLM to generate commit messages and automate the entire process. Key features include AI-assisted git commit and push, eliminating the need for the 'git add .' command, and customizable OpenAI model selection. The plugin supports multiple languages, making it accessible to developers worldwide. Additionally, it offers advanced settings for specifying the OpenAI API key, base URL, and conventional commit format. Developers can contribute to the project by following the provided development instructions.

Pipecat is an open-source framework designed for building generative AI voice bots and multimodal assistants. It provides code building blocks for interacting with AI services, creating low-latency data pipelines, and transporting audio, video, and events over the Internet. Pipecat supports various AI services like speech-to-text, text-to-speech, image generation, and vision models. Users can implement new services and contribute to the framework. Pipecat aims to simplify the development of applications like personal coaches, meeting assistants, customer support bots, and more by providing a complete framework for integrating AI services.

LLM is a Rust library that allows users to utilize multiple LLM backends (OpenAI, Anthropic, Ollama, DeepSeek, xAI, Phind, Groq, Google) in a single project. It provides a unified API and builder style for creating chat or text completion requests without the need for multiple structures and crates. Key features include multi-backend management, multi-step chains, templates for complex prompts, builder pattern for easy configuration, extensibility, validation, evaluation, parallel evaluation, function calling, REST API support, vision integration, and reasoning capabilities.

AI Elements is a component library built on top of shadcn/ui to help build AI-native applications faster. It provides pre-built, customizable React components specifically designed for AI applications, including conversations, messages, code blocks, reasoning displays, and more. The CLI makes it easy to add these components to your Next.js project.

The GPT-RAG Data Ingestion service automates processing of diverse document types for indexing in Azure AI Search. It uses intelligent chunking strategies tailored to each format, generates text and image embeddings, and enables rich, multimodal retrieval experiences for agent-based RAG applications. Supported data sources include Blob Storage, NL2SQL Metadata, and SharePoint. The service selects chunkers based on file extension, such as DocAnalysisChunker for PDF files, OCR for image files, LangChainChunker for text-based files, TranscriptionChunker for video transcripts, and SpreadsheetChunker for spreadsheets. Deployment requires provisioning infrastructure and assigning specific roles to the user or service principal.

SuperDuperDB is a Python framework for integrating AI models, APIs, and vector search engines directly with your existing databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning. Build, deploy and manage any AI application without the need for complex pipelines, infrastructure as well as specialized vector databases, and moving our data there, by integrating AI at your data's source: - Generative AI, LLMs, RAG, vector search - Standard machine learning use-cases (classification, segmentation, regression, forecasting recommendation etc.) - Custom AI use-cases involving specialized models - Even the most complex applications/workflows in which different models work together SuperDuperDB is **not** a database. Think `db = superduper(db)`: SuperDuperDB transforms your databases into an intelligent platform that allows you to leverage the full AI and Python ecosystem. A single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.

The FuzzyAI Fuzzer is a powerful tool for automated LLM fuzzing, designed to help developers and security researchers identify jailbreaks and mitigate potential security vulnerabilities in their LLM APIs. It supports various fuzzing techniques, provides input generation capabilities, can be easily integrated into existing workflows, and offers an extensible architecture for customization and extension. The tool includes attacks like ArtPrompt, Taxonomy-based paraphrasing, Many-shot jailbreaking, Genetic algorithm, Hallucinations, DAN (Do Anything Now), WordGame, Crescendo, ActorAttack, Back To The Past, Please, Thought Experiment, and Default. It supports models from providers like Anthropic, OpenAI, Gemini, Azure, Bedrock, AI21, and Ollama, with the ability to add support for newer models. The tool also supports various cloud APIs and datasets for testing and experimentation.

Gollama is a delightful tool that brings Ollama, your offline conversational AI companion, directly into your terminal. It provides a fun and interactive way to generate responses from various models without needing internet connectivity. Whether you're brainstorming ideas, exploring creative writing, or just looking for inspiration, Gollama is here to assist you. The tool offers an interactive interface, customizable prompts, multiple models selection, and visual feedback to enhance user experience. It can be installed via different methods like downloading the latest release, using Go, running with Docker, or building from source. Users can interact with Gollama through various options like specifying a custom base URL, prompt, model, and enabling raw output mode. The tool supports different modes like interactive, piped, CLI with image, and TUI with image. Gollama relies on third-party packages like bubbletea, glamour, huh, and lipgloss. The roadmap includes implementing piped mode, support for extracting codeblocks, copying responses/codeblocks to clipboard, GitHub Actions for automated releases, and downloading models directly from Ollama using the rest API. Contributions are welcome, and the project is licensed under the MIT License.

DaoCloud Enterprise 5.0 Documentation provides detailed information on using DaoCloud, a Certified Kubernetes Service Provider. The documentation covers current and legacy versions, workflow control using GitOps, and instructions for opening a PR and previewing changes locally. It also includes naming conventions, writing tips, references, and acknowledgments to contributors. Users can find guidelines on writing, contributing, and translating pages, along with using tools like MkDocs, Docker, and Poetry for managing the documentation.

ClearML Fractional GPU is a tool designed to optimize GPU resource utilization by allowing multiple containers to run on the same GPU with driver-level memory limitation and compute time-slicing. It supports CUDA 11.x & CUDA 12.x, preventing greedy processes from grabbing the entire GPU memory. The tool offers options like Dynamic GPU Slicing, Container-based Memory Limits, and Kubernetes-based Static MIG Slicing to enhance hardware utilization and workload performance for AI development.

Evalchemy is a unified and easy-to-use toolkit for evaluating language models, focusing on post-trained models. It integrates multiple existing benchmarks such as RepoBench, AlpacaEval, and ZeroEval. Key features include unified installation, parallel evaluation, simplified usage, and results management. Users can run various benchmarks with a consistent command-line interface and track results locally or integrate with a database for systematic tracking and leaderboard submission.

Vision Parse is a tool that leverages Vision Language Models to parse PDF documents into beautifully formatted markdown content. It offers smart content extraction, content formatting, multi-LLM support, PDF document support, and local model hosting using Ollama. Users can easily convert PDFs to markdown with high precision and preserve document hierarchy and styling. The tool supports multiple Vision LLM providers like OpenAI, LLama, and Gemini for accuracy and speed, making document processing efficient and effortless.

Lecca.io is an AI platform that enables users to configure and deploy Large Language Models (LLMs) with customizable tools and workflows. Users can easily build, customize, and automate AI agents for various tasks. The platform offers features like custom LLM configuration, tool integration, workflow builder, built-in RAG functionalities, and the ability to create custom apps and triggers. Users can also automate LLMs by setting up triggers for autonomous operation. Lecca.io provides documentation for concepts, local development, creating custom apps, adding AI providers, and running Ollama locally. Contributions are welcome, and the platform is distributed under the Apache-2.0 License with Commons Clause, with enterprise features available under a Commercial License.

Kirara AI is a chatbot that supports mainstream language models and chat platforms. It features various functionalities such as image sending, keyword-triggered replies, multi-account support, content moderation, personality settings, and support for platforms like QQ, Telegram, Discord, and WeChat. It also offers HTTP server capabilities, plugin support, conditional triggers, admin commands, drawing models, voice replies, multi-turn conversations, cross-platform message sending, and custom workflows. The tool can be accessed via HTTP API for integration with other platforms.

AgentIQ is a flexible library designed to seamlessly integrate enterprise agents with various data sources and tools. It enables true composability by treating agents, tools, and workflows as simple function calls. With features like framework agnosticism, reusability, rapid development, profiling, observability, evaluation system, user interface, and MCP compatibility, AgentIQ empowers developers to move quickly, experiment freely, and ensure reliability across agent-driven projects.

TypeTale is an AIGC creation software designed specifically for content creators, primarily used for novel promotion. It offers a wide range of AI capabilities such as image, video, and audio generation, as well as text processing and story extraction. The tool also provides workflow customization, AI assistant support, and a vast library of creative materials. With a user-friendly interface and system requirements compatible with Windows operating systems, TypeTale aims to streamline the content creation process for writers and creators.

Clai is a command line context-feeder for AI tasks, supporting MCP client, vendor agnosticism, conversations, rate limit circumvention, profiles, and Unix-like functionality. Users can easily combine and tweak features for diverse use cases. Supported vendors include OpenAI, Anthropic, Mistral, Deepseek, Novita AI, Ollama, and Inception. Users need API keys for model access. Installation via 'go install' or setup script. 'clai help' provides guidance on usage. Glow can be installed for formatted markdown output.

Unofficial HuggingChat Python API for creating chatbots, supporting features like image generation, web search, memorizing context, and changing LLMs. Users can log in, chat with the ChatBot, perform web searches, create new conversations, manage conversations, switch models, get conversation info, use assistants, and delete conversations. The API also includes a CLI mode with various commands for interacting with the tool. Users are advised not to use the application for high-stakes decisions or advice and to avoid high-frequency requests to preserve server resources.

Elia is a powerful terminal user interface designed for interacting with large language models. It allows users to chat with models like Claude 3, ChatGPT, Llama 3, Phi 3, Mistral, and Gemma. Conversations are stored locally in a SQLite database, ensuring privacy. Users can run local models through 'ollama' without data leaving their machine. Elia offers easy installation with pipx and supports various environment variables for different models. It provides a quick start to launch chats and manage local models. Configuration options are available to customize default models, system prompts, and add new models. Users can import conversations from ChatGPT and wipe the database when needed. Elia aims to enhance user experience in interacting with language models through a user-friendly interface.

E.D.D.I (Enhanced Dialog Driven Interface) is an enterprise-certified chatbot middleware that offers advanced prompt and conversation management for Conversational AI APIs. Developed in Java using Quarkus, it is lean, RESTful, scalable, and cloud-native. E.D.D.I is highly scalable and designed to efficiently manage conversations in AI-driven applications, with seamless API integration capabilities. Notable features include configurable NLP and Behavior rules, support for multiple chatbots running concurrently, and integration with MongoDB, OAuth 2.0, and HTML/CSS/JavaScript for UI. The project requires Java 21, Maven 3.8.4, and MongoDB >= 5.0 to run. It can be built as a Docker image and deployed using Docker or Kubernetes, with additional support for integration testing and monitoring through Prometheus and Kubernetes endpoints.

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.