GradeLab's AI assistant provides an efficient solution for grading handwritten exams. Teachers can upload scanned answer sheets, which the AI converts into digital data. Using Large Language Models (LLMs), the text is processed against a predefined answer key, generating grades and feedback. This automated system saves time, increases grading accuracy, and provides comprehensive feedback for students. It also offers real-time performance tracking and data-driven analytics, helping teachers identify student strengths and areas that need improvement. GradeLab ensures consistent and objective grading, revolutionizing the traditional grading process with advanced AI technology.

Minerva is an extensible AI agent framework designed to automate complex workflows using large language models. Developers can integrate external tools—such as web search, API calls, or file processors—define custom planning strategies, and manage conversational or persistent memory. Minerva supports both synchronous and asynchronous task execution, configurable logging, and a plugin architecture, making it easy to prototype, test, and deploy intelligent agents capable of reasoning, planning, and tool use in real-world scenarios.

ToolAgents is a modular open-source AI agent framework that integrates large language models with external tools to automate complex workflows. Developers register tools via a centralized registry, defining endpoints for tasks such as API calls, database queries, code execution, and document analysis. Agents can plan multi-step operations, dynamically invoking or chaining tools based on LLM outputs. The framework supports both sequential and parallel task execution, error handling, and extensible plug-ins for custom tool integrations. With Python-based APIs, ToolAgents simplifies building, testing, and deploying intelligent agents that fetch data, generate content, execute scripts, and process documents, enabling rapid prototyping and scalable automation across analytics, research, and business operations.

X Search Assistant is an AI-powered tool designed to help users craft advanced Twitter searches. With this tool, you don't need to memorize complex search operators. Simply type your query in plain English, and the LLM (Large Language Model) will generate the corresponding search query for Twitter. You can choose from a variety of supported LLMs and customize them according to your needs. The tool also provides shortcuts and flags to enhance your search efficiency, making Twitter research easier and more effective.

AI Agent FletUI provides a modular UI framework for creating intelligent chat applications backed by large language models. It bundles chat widgets, tool integration panels, memory stores and event handlers that connect seamlessly with any LLM provider. Users can define custom tools, manage session context persistently and render rich message formats out of the box. The library abstracts the complexity of UI layout in Flet and streamlines tool invocation, enabling rapid prototyping and deployment of LLM-driven assistants.

GPTBots is an advanced AI platform that allows businesses to create and deploy highly adaptable AI bots. These bots leverage large language models (LLMs) to connect with enterprise data, services, and workflows, providing robust AI solutions. By utilizing GPTBots, companies can enhance customer service, automate tasks, and streamline operations through intelligent bot interactions, leading to increased efficiency and productivity.

LlamaIndex is an innovative framework that empowers developers to create applications that leverage large language models. By providing tools to connect custom data sources, LlamaIndex ensures your data is utilized effectively in generative AI applications. It supports various formats and data types, enabling seamless integration and management of both private and public data sources. This makes it easier to build intelligent applications that accurately respond to user queries or perform tasks using contextual data, thus enhancing operational efficiency.

Reflection 70B is an innovative large language model (LLM) developed by HyperWrite that leverages the groundbreaking Reflection-Tuning technology. This model not only generates text but also analyzes its output, allowing it to identify and rectify mistakes on the fly. Its architecture is based on Meta's Llama framework, featuring 70 billion parameters. With enhanced reasoning capabilities, Reflection 70B provides a more reliable, context-aware conversational experience. The model is designed to adapt and improve continuously, making it suitable for various applications in natural language processing.

The AWS Sample Model Context Protocol Demos is an open-source repository showcasing standardized patterns for Large Language Model (LLM) context management and tool invocation. It features two complete demos—one in JavaScript/TypeScript and one in Python—that implement the Model Context Protocol, enabling developers to build AI agents that call AWS Lambda functions, preserve conversation history, and stream responses. Sample code demonstrates message formatting, function argument serialization, error handling, and customizable tool integrations, accelerating prototyping of generative AI applications.