Magic is an open-source all-in-one AI productivity platform designed to help enterprises quickly build and deploy AI applications, aiming for a 100x increase in productivity. It consists of various AI products and infrastructure tools, such as Super Magic, Magic IM, Magic Flow, and more. Super Magic is a general-purpose AI Agent for complex task scenarios, while Magic Flow is a visual AI workflow orchestration system. Magic IM is an enterprise-grade AI Agent conversation system for internal knowledge management. Teamshare OS is a collaborative office platform integrating AI capabilities. The platform provides cloud services, enterprise solutions, and a self-hosted community edition for users to leverage its features.

The AI Platform Engineering repository provides a collection of tools and resources for building and deploying AI models. It includes libraries for data preprocessing, model training, and model serving. The repository also contains example code and tutorials to help users get started with AI development. Whether you are a beginner or an experienced AI engineer, this repository offers valuable insights and best practices to streamline your AI projects.

Refly.AI is an open-source AI-native creation engine that empowers users to transform ideas into production-ready content. It features a free-form canvas interface with multi-threaded conversations, knowledge base integration, contextual memory, intelligent search, WYSIWYG AI editor, and more. Users can leverage AI-powered capabilities, context memory, knowledge base integration, quotes, and AI document editing to enhance their content creation process. Refly offers both cloud and self-hosting options, making it suitable for individuals, enterprises, and organizations. The tool is designed to facilitate human-AI collaboration and streamline content creation workflows.

Dapr Agents is a developer framework for building production-grade resilient AI agent systems that operate at scale. It enables software developers to create AI agents that reason, act, and collaborate using Large Language Models (LLMs), while providing built-in observability and stateful workflow execution to ensure agentic workflows complete successfully. The framework is scalable, efficient, Kubernetes-native, data-driven, secure, observable, vendor-neutral, and open source. It offers features like scalable workflows, cost-effective AI adoption, data-centric AI agents, accelerated development, integrated security and reliability, built-in messaging and state infrastructure, and vendor-neutral and open source support. Dapr Agents is designed to simplify the development of AI applications and workflows by providing a comprehensive API surface and seamless integration with various data sources and services.

Weam is an open source platform designed to help teams systematically adopt AI. It provides a production-ready stack with Next.js frontend and Node.js/Python backend, allowing for immediate deployment and use. Weam connects to major LLM providers, enabling easy access to the latest AI models. The platform organizes AI interactions into 'Brains' for different departments, offering customization and expansion options. Features include chat system, productivity tools, sharing & access controls, prompt library, AI agents, RAG, MCP, enterprise features, pre-built automations, and upcoming AI app solutions. Weam is free, open source, and scalable to meet growing needs.

LLMariner is an extensible open source platform built on Kubernetes to simplify the management of generative AI workloads. It enables efficient handling of training and inference data within clusters, with OpenAI-compatible APIs for seamless integration with a wide range of AI-driven applications.

The UltraRAG framework is a researcher and developer-friendly RAG system solution that simplifies the process from data construction to model fine-tuning in domain adaptation. It introduces an automated knowledge adaptation technology system, supporting no-code programming, one-click synthesis and fine-tuning, multidimensional evaluation, and research-friendly exploration work integration. The architecture consists of Frontend, Service, and Backend components, offering flexibility in customization and optimization. Performance evaluation in the legal field shows improved results compared to VanillaRAG, with specific metrics provided. The repository is licensed under Apache-2.0 and encourages citation for support.

Upsonic offers a cutting-edge enterprise-ready framework for orchestrating LLM calls, agents, and computer use to complete tasks cost-effectively. It provides reliable systems, scalability, and a task-oriented structure for real-world cases. Key features include production-ready scalability, task-centric design, MCP server support, tool-calling server, computer use integration, and easy addition of custom tools. The framework supports client-server architecture and allows seamless deployment on AWS, GCP, or locally using Docker.

Neuro SAN Studio is an open-source library for building agent networks across various industries. It simplifies the development of collaborative AI systems by enabling users to create sophisticated multi-agent applications using declarative configuration files. The tool offers features like data-driven configuration, adaptive communication protocols, safe data handling, dynamic agent network designer, flexible tool integration, robust traceability, and cloud-agnostic deployment. It has been used in various use-cases such as automated generation of multi-agent configurations, airline policy assistance, banking operations, market analysis in consumer packaged goods, insurance claims processing, intranet knowledge management, retail operations, telco network support, therapy vignette supervision, and more.

CodeGate is a local gateway that enhances the safety of AI coding assistants by ensuring AI-generated recommendations adhere to best practices, safeguarding code integrity, and protecting individual privacy. Developed by Stacklok, CodeGate allows users to confidently leverage AI in their development workflow without compromising security or productivity. It works seamlessly with coding assistants, providing real-time security analysis of AI suggestions. CodeGate is designed with privacy at its core, keeping all data on the user's machine and offering complete control over data.

JamAI Base is an open-source platform integrating SQLite and LanceDB databases with managed memory and RAG capabilities. It offers built-in LLM, vector embeddings, and reranker orchestration accessible through a spreadsheet-like UI and REST API. Users can transform static tables into dynamic entities, facilitate real-time interactions, manage structured data, and simplify chatbot development. The tool focuses on ease of use, scalability, flexibility, declarative paradigm, and innovative RAG techniques, making complex data operations accessible to users with varying technical expertise.

ChatFAQ is an open-source comprehensive platform for creating a wide variety of chatbots: generic ones, business-trained, or even capable of redirecting requests to human operators. It includes a specialized NLP/NLG engine based on a RAG architecture and customized chat widgets, ensuring a tailored experience for users and avoiding vendor lock-in.

Firebase Genkit (beta) is a framework with powerful tooling to help app developers build, test, deploy, and monitor AI-powered features with confidence. Genkit is cloud optimized and code-centric, integrating with many services that have free tiers to get started. It provides unified API for generation, context-aware AI features, evaluation of AI workflow, extensibility with plugins, easy deployment to Firebase or Google Cloud, observability and monitoring with OpenTelemetry, and a developer UI for prototyping and testing AI features locally. Genkit works seamlessly with Firebase or Google Cloud projects through official plugins and templates.

BeeAI is an open-source platform that simplifies the discovery, running, and sharing of AI agents across different frameworks. It addresses challenges such as framework fragmentation, deployment complexity, and discovery issues by providing a standardized platform for individuals and teams to access agents easily. With features like a centralized agent catalog, framework-agnostic interfaces, containerized agents, and consistent user experiences, BeeAI aims to streamline the process of working with AI agents for both developers and teams.

Akeru.ai is an open-source AI platform leveraging the power of decentralization. It offers transparent, safe, and highly available AI capabilities. The platform aims to give developers access to open-source and transparent AI resources through its decentralized nature hosted on an edge network. Akeru API introduces features like retrieval, function calling, conversation management, custom instructions, data input optimization, user privacy, testing and iteration, and comprehensive documentation. It is ideal for creating AI agents and enhancing web and mobile applications with advanced AI capabilities. The platform runs on a Bittensor Subnet design that aims to democratize AI technology and promote an equitable AI future. Akeru.ai embraces decentralization challenges to ensure a decentralized and equitable AI ecosystem with security features like watermarking and network pings. The API architecture integrates with technologies like Bun, Redis, and Elysia for a robust, scalable solution.

Genkit is an open-source framework for building full-stack AI-powered applications, used in production by Google's Firebase. It provides SDKs for JavaScript/TypeScript (Stable), Go (Beta), and Python (Alpha) with unified interface for integrating AI models from providers like Google, OpenAI, Anthropic, Ollama. Rapidly build chatbots, automations, and recommendation systems using streamlined APIs for multimodal content, structured outputs, tool calling, and agentic workflows. Genkit simplifies AI integration with open-source SDK, unified APIs, and offers text and image generation, structured data generation, tool calling, prompt templating, persisted chat interfaces, AI workflows, and AI-powered data retrieval (RAG).

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

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.

The Spring AI project provides a Spring-friendly API and abstractions for developing AI applications. It offers a portable client API for interacting with generative AI models, enabling developers to easily swap out implementations and access various models like OpenAI, Azure OpenAI, and HuggingFace. Spring AI also supports prompt engineering, providing classes and interfaces for creating and parsing prompts, as well as incorporating proprietary data into generative AI without retraining the model. This is achieved through Retrieval Augmented Generation (RAG), which involves extracting, transforming, and loading data into a vector database for use by AI models. Spring AI's VectorStore abstraction allows for seamless transitions between different vector database implementations.

RAGStack is an out-of-the-box solution simplifying Retrieval Augmented Generation (RAG) in GenAI apps. RAGStack includes the best open-source for implementing RAG, giving developers a comprehensive Gen AI Stack leveraging LangChain, CassIO, and more. RAGStack leverages the LangChain ecosystem and is fully compatible with LangSmith for monitoring your AI deployments.

Breadboard is a library for prototyping generative AI applications. It is inspired by the hardware maker community and their boundless creativity. Breadboard makes it easy to wire prototypes and share, remix, reuse, and compose them. The library emphasizes ease and flexibility of wiring, as well as modularity and composability.

Cloudflare-ai-web is a lightweight and easy-to-use tool that allows you to quickly deploy a multi-modal AI platform using Cloudflare Workers AI. It supports serverless deployment, password protection, and local storage of chat logs. With a size of only ~638 kB gzip, it is a great option for building AI-powered applications without the need for a dedicated server.

AppBuilder SDK is a one-stop development tool for AI native applications, providing basic cloud resources, AI capability engine, Qianfan large model, and related capability components to improve the development efficiency of AI native applications.

This repository contains community-driven practical examples of building AI applications and solving various tasks with AI using open-source tools and models. Everyone is welcome to contribute, and we value everybody's contribution! There are several ways you can contribute to the Open-Source AI Cookbook: Submit an idea for a desired example/guide via GitHub Issues. Contribute a new notebook with a practical example. Improve existing examples by fixing issues/typos. Before contributing, check currently open issues and pull requests to avoid working on something that someone else is already working on.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.