EvalKit is an open-source TypeScript library for evaluating and improving the performance of large language models (LLMs). It helps developers ensure the reliability, accuracy, and trustworthiness of their AI models. The library provides various metrics such as Bias Detection, Coherence, Faithfulness, Hallucination, Intent Detection, and Semantic Similarity. EvalKit is designed to be user-friendly with detailed documentation, tutorials, and recipes for different use cases and LLM providers. It requires Node.js 18+ and an OpenAI API Key for installation and usage. Contributions from the community are welcome under the Apache 2.0 License.

The Arthur Engine is a comprehensive tool for monitoring and governing AI/ML workloads. It provides evaluation and benchmarking of machine learning models, guardrails enforcement, and extensibility for fitting into various application architectures. With support for a wide range of evaluation metrics and customizable features, the tool aims to improve model understanding, optimize generative AI outputs, and prevent data-security and compliance risks. Key features include real-time guardrails, model performance monitoring, feature importance visualization, error breakdowns, and support for custom metrics and models integration.

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.

LazyLLM is a low-code development tool for building complex AI applications with multiple agents. It assists developers in building AI applications at a low cost and continuously optimizing their performance. The tool provides a convenient workflow for application development and offers standard processes and tools for various stages of application development. Users can quickly prototype applications with LazyLLM, analyze bad cases with scenario task data, and iteratively optimize key components to enhance the overall application performance. LazyLLM aims to simplify the AI application development process and provide flexibility for both beginners and experts to create high-quality applications.

fenic is an opinionated DataFrame framework from typedef.ai for building AI and agentic applications. It transforms unstructured and structured data into insights using familiar DataFrame operations enhanced with semantic intelligence. With support for markdown, transcripts, and semantic operators, plus efficient batch inference across various model providers. fenic is purpose-built for LLM inference, providing a query engine designed for AI workloads, semantic operators as first-class citizens, native unstructured data support, production-ready infrastructure, and a familiar DataFrame API.

Pluto is a development tool dedicated to helping developers **build cloud and AI applications more conveniently** , resolving issues such as the challenging deployment of AI applications and open-source models. Developers are able to write applications in familiar programming languages like **Python and TypeScript** , **directly defining and utilizing the cloud resources necessary for the application within their code base** , such as AWS SageMaker, DynamoDB, and more. Pluto automatically deduces the infrastructure resource needs of the app through **static program analysis** and proceeds to create these resources on the specified cloud platform, **simplifying the resources creation and application deployment process**.

The NVIDIA AI Blueprint for Video Search and Summarization is a repository showcasing video search and summarization agent with NVIDIA NIM microservices. It enables industries to make better decisions faster by providing insightful, accurate, and interactive video analytics AI agents. These agents can perform tasks like video summarization and visual question-answering, unlocking new application possibilities. The repository includes software components like NIM microservices, ingestion pipeline, and CA-RAG module, offering a comprehensive solution for analyzing and summarizing large volumes of video data. The target audience includes video analysts, IT engineers, and GenAI developers who can benefit from the blueprint's 1-click deployment steps, easy-to-manage configurations, and customization options. The repository structure overview includes directories for deployment, source code, and training notebooks, along with documentation for detailed instructions. Hardware requirements vary based on deployment topology and dependencies like VLM and LLM, with different deployment methods such as Launchable Deployment, Docker Compose Deployment, and Helm Chart Deployment provided for various use cases.

Interaqt is a project that aims to separate application business logic from its specific implementation by providing a structured data model and tools to automatically decide and implement software architecture. It liberates individuals and teams from implementation specifics, performance requirements, and cost demands, allowing them to focus on articulating business logic. The approach is considered optimal in the era of large language models (LLMs) as it eliminates uncertainty in generated systems and enables independence from engineering involvement unless specific capabilities are required.

The .NET AI Workshop is a comprehensive guide designed to help developers add AI features to .NET applications. It covers various AI-based features such as classification, summarization, data extraction/cleaning, anomaly detection, translation, sentiment detection, semantic search, Q&A chatbots, and voice assistants. The workshop is tailored for developers new to AI in .NET applications, focusing on the usage of AI services without the need for prior AI technology knowledge. It provides examples using .NET and C# with a focus on AI topics, aiming to enhance productivity and automation in applications.

NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). The primary objective of NeMo is to provide a scalable framework for researchers and developers from industry and academia to more easily implement and design new generative AI models by being able to leverage existing code and pretrained models.

AgentMark is a tool designed to make it easy for developers to develop, test, and evaluate AI Agents. It combines Markdown syntax with JSX components to create reliable Agents. The tool seamlessly integrates with SDKs, offering comprehensive tooling such as full type safety, unified prompt configuration, syntax highlighting, loops and conditionals, custom SDK adapters, and support for text, object, image, and speech generation across multiple model providers.

NVIDIA NeMo Framework is a scalable and cloud-native generative AI framework built for researchers and PyTorch developers working on Large Language Models (LLMs), Multimodal Models (MMs), Automatic Speech Recognition (ASR), Text to Speech (TTS), and Computer Vision (CV) domains. It is designed to help you efficiently create, customize, and deploy new generative AI models by leveraging existing code and pre-trained model checkpoints.

Craftgen.ai is an innovative AI platform designed for both technical and non-technical users. It's built on a foundation of graph architecture for scalability and the Actor Model for efficient concurrent operations, tailored to both technical and non-technical users. A key aspect of Craftgen.ai is its modular AI approach, allowing users to assemble and customize AI components like building blocks to fit their specific needs. The platform's robustness is enhanced by its event-driven architecture, ensuring reliable data processing and featuring browser web technologies for universal access. Craftgen.ai excels in dynamic tool and workflow generation, with strong offline capabilities for secure environments and plans for desktop application integration. A unique and valuable feature of Craftgen.ai is its marketplace, where users can access a variety of pre-built AI solutions. This marketplace accelerates the deployment of AI tools but also fosters a community of sharing and innovation. Users can contribute to and leverage this repository of solutions, enhancing the platform's versatility and practicality. Craftgen.ai uses JSON schema for industry-standard alignment, enabling seamless integration with any API following the OpenAPI spec. This allows for a broad range of applications, from automating data analysis to streamlining content management. The platform is designed to bridge the gap between advanced AI technology and practical usability. It's a flexible, secure, and intuitive platform that empowers users, from developers seeking to create custom AI solutions to businesses looking to automate routine tasks. Craftgen.ai's goal is to make AI technology an integral, seamless part of everyday problem-solving and innovation, providing a platform where modular AI and a thriving marketplace converge to meet the diverse needs of its users.

LLMStats is a community-driven repository providing detailed information on hundreds of Language Models (LLMs). Users can compare and explore LLMs through an interactive dashboard at llm-stats.com. The repository includes model parameters, context window sizes, licensing details, capabilities, provider pricing, performance metrics, and standardized benchmark results. Community contributions are welcome to maintain data accuracy. The platform prioritizes data quality through verifiable source links, community review processes, multiple source citations, and regular data validation. While not guaranteed to be 100% accurate, efforts are made to ensure the information is as reliable as possible.

NVIDIA NeMo Agent toolkit is a flexible, lightweight, and unifying library that allows you to easily connect existing enterprise agents to data sources and tools across any framework. It is framework agnostic, promotes reusability, enables rapid development, provides profiling capabilities, offers observability features, includes an evaluation system, features a user interface for interaction, and supports the Model Context Protocol (MCP). With NeMo Agent toolkit, users can move quickly, experiment freely, and ensure reliability across all agent-driven projects.

CosmosAIGraph is an AI-powered graph and RAG implementation of OmniRAG pattern, utilizing Azure Cosmos DB and other sources. It includes presentations, reference application documentation, FAQs, and a reference dataset of Python libraries pre-vectorized. The project focuses on Azure Cosmos DB for NoSQL and Apache Jena implementation for the in-memory RDF graph. It provides DockerHub images, with plans to add RBAC and Microsoft Entra ID/AAD authentication support, update AI model to gpt-4.5, and offer generic graph examples with a graph generation solution.

EvalKit is an open-source TypeScript library for evaluating and improving the performance of large language models (LLMs). It helps developers ensure the reliability, accuracy, and trustworthiness of their AI models. The library provides various metrics such as Bias Detection, Coherence, Faithfulness, Hallucination, Intent Detection, and Semantic Similarity. EvalKit is designed to be user-friendly with detailed documentation, tutorials, and recipes for different use cases and LLM providers. It requires Node.js 18+ and an OpenAI API Key for installation and usage. Contributions from the community are welcome under the Apache 2.0 License.

The AI Fairness 360 toolkit is an open-source library designed to detect and mitigate bias in machine learning models. It provides a comprehensive set of metrics, explanations, and algorithms for bias mitigation in various domains such as finance, healthcare, and education. The toolkit supports multiple bias mitigation algorithms and fairness metrics, and is available in both Python and R. Users can leverage the toolkit to ensure fairness in AI applications and contribute to its development for extensibility.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

Promptfoo is a tool for testing and evaluating LLM output quality. With promptfoo, you can build reliable prompts, models, and RAGs with benchmarks specific to your use-case, speed up evaluations with caching, concurrency, and live reloading, score outputs automatically by defining metrics, use as a CLI, library, or in CI/CD, and use OpenAI, Anthropic, Azure, Google, HuggingFace, open-source models like Llama, or integrate custom API providers for any LLM API.

Vespa is a platform that performs operations such as selecting a subset of data in a large corpus, evaluating machine-learned models over the selected data, organizing and aggregating it, and returning it, typically in less than 100 milliseconds, all while the data corpus is continuously changing. It has been in development for many years and is used on a number of large internet services and apps which serve hundreds of thousands of queries from Vespa per second.

The Vertex AI SDK for Python is a library that provides a convenient way to use the Vertex AI API. It offers a high-level interface for creating and managing Vertex AI resources, such as datasets, models, and endpoints. The SDK also provides support for training and deploying custom models, as well as using AutoML models. With the Vertex AI SDK for Python, you can quickly and easily build and deploy machine learning models on Vertex AI.

ScandEval is a framework for evaluating pretrained language models on mono- or multilingual language tasks. It provides a unified interface for benchmarking models on a variety of tasks, including sentiment analysis, question answering, and machine translation. ScandEval is designed to be easy to use and extensible, making it a valuable tool for researchers and practitioners alike.

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: * Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. * Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. * Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models. * Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded! * Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

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.