Holistic AI is an open-source library dedicated to assessing and improving the trustworthiness of AI systems. It focuses on measuring and mitigating bias, explainability, robustness, security, and efficacy in AI models. The tool provides comprehensive metrics, mitigation techniques, a user-friendly interface, and visualization tools to enhance AI system trustworthiness. It offers documentation, tutorials, and detailed installation instructions for easy integration into existing workflows.

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.

TaskingAI brings Firebase's simplicity to **AI-native app development**. The platform enables the creation of GPTs-like multi-tenant applications using a wide range of LLMs from various providers. It features distinct, modular functions such as Inference, Retrieval, Assistant, and Tool, seamlessly integrated to enhance the development process. TaskingAI’s cohesive design ensures an efficient, intelligent, and user-friendly experience in AI application development.

Paperless-AI is an automated document analyzer tool designed for Paperless-ngx users. It utilizes the OpenAI API and Ollama (Mistral, llama, phi 3, gemma 2) to automatically scan, analyze, and tag documents. The tool offers features such as automatic document scanning, AI-powered document analysis, automatic title and tag assignment, manual mode for analyzing documents, easy setup through a web interface, document processing dashboard, error handling, and Docker support. Users can configure the tool through a web interface and access a debug interface for monitoring and troubleshooting. Paperless-AI aims to streamline document organization and analysis processes for users with access to Paperless-ngx and AI capabilities.

MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

Motia is an AI agent framework designed for software engineers to create, test, and deploy production-ready AI agents quickly. It provides a code-first approach, allowing developers to write agent logic in familiar languages and visualize execution in real-time. With Motia, developers can focus on business logic rather than infrastructure, offering zero infrastructure headaches, multi-language support, composable steps, built-in observability, instant APIs, and full control over AI logic. Ideal for building sophisticated agents and intelligent automations, Motia's event-driven architecture and modular steps enable the creation of GenAI-powered workflows, decision-making systems, and data processing pipelines.

The Payload AI Plugin is an advanced extension that integrates modern AI capabilities into your Payload CMS, streamlining content creation and management. It offers features like text generation, voice and image generation, field-level prompt customization, prompt editor, document analyzer, fact checking, automated content workflows, internationalization support, editor AI suggestions, and AI chat support. Users can personalize and configure the plugin by setting environment variables. The plugin is actively developed and tested with Payload version v3.2.1, with regular updates expected.

The Agentic Radar is a security scanner designed to analyze and assess agentic systems for security and operational insights. It helps users understand how agentic systems function, identify potential vulnerabilities, and create security reports. The tool includes workflow visualization, tool identification, and vulnerability mapping, providing a comprehensive HTML report for easy reviewing and sharing. It simplifies the process of assessing complex workflows and multiple tools used in agentic systems, offering a structured view of potential risks and security frameworks.

OpenManus-RL is an open-source initiative focused on enhancing reasoning and decision-making capabilities of large language models (LLMs) through advanced reinforcement learning (RL)-based agent tuning. The project explores novel algorithmic structures, diverse reasoning paradigms, sophisticated reward strategies, and extensive benchmark environments. It aims to push the boundaries of agent reasoning and tool integration by integrating insights from leading RL tuning frameworks and continuously updating progress in a dynamic, live-streaming fashion.

AirLLM is a tool that optimizes inference memory usage, enabling large language models to run on low-end GPUs without quantization, distillation, or pruning. It supports models like Llama3.1 on 8GB VRAM. The tool offers model compression for up to 3x inference speedup with minimal accuracy loss. Users can specify compression levels, profiling modes, and other configurations when initializing models. AirLLM also supports prefetching and disk space management. It provides examples and notebooks for easy implementation and usage.

Slips is a powerful endpoint behavioral intrusion prevention and detection system that uses machine learning to detect malicious behaviors in network traffic. It can work with network traffic in real-time, PCAP files, and network flows from tools like Suricata, Zeek/Bro, and Argus. Slips threat detection is based on machine learning models, threat intelligence feeds, and expert heuristics. It gathers evidence of malicious behavior and triggers alerts when enough evidence is accumulated. The tool is Python-based and supported on Linux and MacOS, with blocking features only on Linux. Slips relies on Zeek network analysis framework and Redis for interprocess communication. It offers a graphical user interface for easy monitoring and analysis.

MathVerse is an all-around visual math benchmark designed to evaluate the capabilities of Multi-modal Large Language Models (MLLMs) in visual math problem-solving. It collects high-quality math problems with diagrams to assess how well MLLMs can understand visual diagrams for mathematical reasoning. The benchmark includes 2,612 problems transformed into six versions each, contributing to 15K test samples. It also introduces a Chain-of-Thought (CoT) Evaluation strategy for fine-grained assessment of output answers.

DriveLM is a multimodal AI model that enables autonomous driving by combining computer vision and natural language processing. It is designed to understand and respond to complex driving scenarios using visual and textual information. DriveLM can perform various tasks related to driving, such as object detection, lane keeping, and decision-making. It is trained on a massive dataset of images and text, which allows it to learn the relationships between visual cues and driving actions. DriveLM is a powerful tool that can help to improve the safety and efficiency of autonomous vehicles.

Mercury is a code efficiency benchmark designed for code synthesis tasks. It includes 1,889 programming tasks of varying difficulty levels and provides test case generators for comprehensive evaluation. The benchmark aims to assess the efficiency of large language models in generating code solutions.

Learn Prompting is a platform offering free resources, courses, and webinars to master prompt engineering and generative AI. It provides a Prompt Engineering Guide, courses on Generative AI, workshops, and the HackAPrompt competition. The platform also offers AI Red Teaming and AI Safety courses, research reports on prompting techniques, and welcomes contributions in various forms such as content suggestions, translations, artwork, and typo fixes. Users can locally develop the website using Visual Studio Code, Git, and Node.js, and run it in development mode to preview changes.

AIBrix is an open-source initiative providing essential building blocks for scalable GenAI inference infrastructure. It delivers a cloud-native solution optimized for deploying, managing, and scaling large language model (LLM) inference, tailored to enterprise needs. Key features include High-Density LoRA Management, LLM Gateway and Routing, LLM App-Tailored Autoscaler, Unified AI Runtime, Distributed Inference, Distributed KV Cache, Cost-efficient Heterogeneous Serving, and GPU Hardware Failure Detection.

Holistic AI is an open-source library dedicated to assessing and improving the trustworthiness of AI systems. It focuses on measuring and mitigating bias, explainability, robustness, security, and efficacy in AI models. The tool provides comprehensive metrics, mitigation techniques, a user-friendly interface, and visualization tools to enhance AI system trustworthiness. It offers documentation, tutorials, and detailed installation instructions for easy integration into existing workflows.

Pathway's LLM (Large Language Model) Apps provide a platform to quickly deploy AI applications using the latest knowledge from data sources. The Python application examples in this repository are Docker-ready, exposing an HTTP API to the frontend. These apps utilize the Pathway framework for data synchronization, API serving, and low-latency data processing without the need for additional infrastructure dependencies. They connect to document data sources like S3, Google Drive, and Sharepoint, offering features like real-time data syncing, easy alert setup, scalability, monitoring, security, and unification of application logic.

Kaytu is an AI platform that enhances cloud efficiency by analyzing historical usage data and providing intelligent recommendations for optimizing instance sizes. Users can pay for only what they need without compromising the performance of their applications. The platform is easy to use with a one-line command, allows customization for specific requirements, and ensures security by extracting metrics from the client side. Kaytu is open-source and supports AWS services, with plans to expand to GCP, Azure, GPU optimization, and observability data from Prometheus in the future.

This repository is a curated list of open-source libraries for production large language models. It includes tools for data preprocessing, training/finetuning, evaluation/benchmarking, serving/inference, application/RAG, testing/monitoring, and guardrails/security. The repository also provides a new category called LLM Cookbook/Examples for showcasing examples and guides on using various LLM APIs.

LangKit is an open-source text metrics toolkit for monitoring language models. It offers methods for extracting signals from input/output text, compatible with whylogs. Features include text quality, relevance, security, sentiment, toxicity analysis. Installation via PyPI. Modules contain UDFs for whylogs. Benchmarks show throughput on AWS instances. FAQs available.

Nesa is a tool that allows users to run on-prem AI for a fraction of the cost through a blind API. It provides blind privacy, zero latency on protected inference, wide model coverage, cost savings compared to cloud and on-prem AI, RAG support, and ChatGPT compatibility. Nesa achieves blind AI through Equivariant Encryption (EE), a new security technology that provides complete inference encryption with no additional latency. EE allows users to perform inference on neural networks without exposing the underlying data, preserving data privacy and security.

Euler's Shield is a decentralized, AI-powered financial system designed to stabilize the value of Pi Coin at $314.159. It combines blockchain, machine learning, and cybersecurity to ensure the security, scalability, and decentralization of the Pi Coin ecosystem.

K8sGateway is a feature-rich, fast, and flexible Kubernetes-native API gateway built on Envoy proxy and Kubernetes Gateway API. It excels in function-level routing, supports legacy apps, microservices, and serverless. It offers robust discovery capabilities, seamless integration with open-source projects, and supports hybrid applications with various technologies, architectures, protocols, and clouds.

Holistic AI is an open-source library dedicated to assessing and improving the trustworthiness of AI systems. It focuses on measuring and mitigating bias, explainability, robustness, security, and efficacy in AI models. The tool provides comprehensive metrics, mitigation techniques, a user-friendly interface, and visualization tools to enhance AI system trustworthiness. It offers documentation, tutorials, and detailed installation instructions for easy integration into existing workflows.

NanoLLM is a tool designed for optimized local inference for Large Language Models (LLMs) using HuggingFace-like APIs. It supports quantization, vision/language models, multimodal agents, speech, vector DB, and RAG. The tool aims to provide efficient and effective processing for LLMs on local devices, enhancing performance and usability for various AI applications.

This repository contains materials for the Microsoft Learn AI Fundamentals module. It covers the basics of artificial intelligence, machine learning, and data science. The content includes hands-on labs, interactive learning modules, and assessments to help learners understand key concepts and techniques in AI. Whether you are new to AI or looking to expand your knowledge, this module provides a comprehensive introduction to the fundamentals of AI.

Awesome AI Tools is a curated list of popular tools and resources for artificial intelligence enthusiasts. It includes a wide range of tools such as machine learning libraries, deep learning frameworks, data visualization tools, and natural language processing resources. Whether you are a beginner or an experienced AI practitioner, this repository aims to provide you with a comprehensive collection of tools to enhance your AI projects and research. Explore the list to discover new tools, stay updated with the latest advancements in AI technology, and find the right resources to support your AI endeavors.

The go2coding.github.io repository is a collection of resources for AI enthusiasts, providing information on AI products, open-source projects, AI learning websites, and AI learning frameworks. It aims to help users stay updated on industry trends, learn from community projects, access learning resources, and understand and choose AI frameworks. The repository also includes instructions for local and external deployment of the project as a static website, with details on domain registration, hosting services, uploading static web pages, configuring domain resolution, and a visual guide to the AI tool navigation website. Additionally, it offers a platform for AI knowledge exchange through a QQ group and promotes AI tools through a WeChat public account.

AI-Notes is a repository dedicated to practical applications of artificial intelligence and deep learning. It covers concepts such as data mining, machine learning, natural language processing, and AI. The repository contains Jupyter Notebook examples for hands-on learning and experimentation. It explores the development stages of AI, from narrow artificial intelligence to general artificial intelligence and superintelligence. The content delves into machine learning algorithms, deep learning techniques, and the impact of AI on various industries like autonomous driving and healthcare. The repository aims to provide a comprehensive understanding of AI technologies and their real-world applications.

Prompt Panel is a tool designed to accelerate the adoption of AI agents by providing a platform where users can run large language models across any inference provider, create custom agent plugins, and use their own data safely. The tool allows users to break free from walled-gardens and have full control over their models, conversations, and logic. With Prompt Panel, users can pair their data with any language model, online or offline, and customize the system to meet their unique business needs without any restrictions.

The 'ai-demos' repository is a collection of example code from presentations focusing on building with AI and LLMs. It serves as a resource for developers looking to explore practical applications of artificial intelligence in their projects. The code snippets showcase various techniques and approaches to leverage AI technologies effectively. The repository aims to inspire and educate developers on integrating AI solutions into their applications.