SciPhi is an open-source platform designed to simplify the building, deploying, and scaling of Retrieval-Augmented Generation (RAG) systems. It provides an end-to-end solution for developers, enabling them to focus on AI innovation without worrying about the underlying infrastructure. With tools for automated knowledge graph extraction, document and user management, and robust observability, SciPhi ensures efficient and optimized RAG system deployment.

SupaLaunch is a meticulously crafted NextJS and Supabase boilerplate, enabling startups to quickly establish a robust web presence. It integrates PostgreSQL, Stripe payments, authentication, storage, and AI tools, among others, to cater to diverse business needs. This starter kit aims to streamline the development process, letting businesses focus on growth rather than infrastructure setup.

Astro Agents provides a modular architecture for building AI agents in JavaScript and TypeScript. Developers can register custom tools for data lookup, integrate memory stores to preserve conversational context, and orchestrate multi-step reasoning workflows. It supports multiple LLM providers such as OpenAI and Hugging Face, and can be deployed as static sites or serverless functions. With built-in observability and extensible plugins, teams can prototype, test, and scale AI-driven assistants without heavy infrastructure overhead.

The Kurtosis AutoGPT Package is an AI Agent framework packaged as a Kurtosis module that delivers a fully configured AutoGPT environment with minimal effort. It provisions and wires up services such as PostgreSQL, Redis, and a vector store, then injects your API keys and agent scripts into the network. Using Docker and Kurtosis CLI, you can spin up isolated agent instances, view logs, adjust budgets, and manage network policies. This package removes infrastructure friction so teams can rapidly develop, test, and scale autonomous GPT-driven workflows in a reproducible manner.

Maintain-AI uses state-of-the-art AI and machine learning to deliver automated road condition assessments. The technology enhances the accuracy and efficiency of road inspections by analyzing collected road network images. This enables cities and municipalities to make data-driven decisions, optimizing their maintenance budgets and improving overall road quality.