About RITA
Recursively Intelligent Temporal Architecture - Building the future of AI agent memory and context management.
Our Mission
RITA represents a new approach to AI agent architecture. Instead of forcing large language models to manage their own context within limited token windows, we treat the prompt as an external environment variable - a Python REPL that the model can programmatically access, search, and modify.
Our Python micro-apps handle the complexity of memory management, temporal awareness, and skill development. The model focuses on reasoning and decision-making while clean, pre-built tools handle storage, retrieval, and context optimization.
The result: agents with effectively infinite memory, persistent identity, and the ability to learn and improve over time. One API key in, one intelligent agent out.
Our Vision
Memory blocks stored in structured SQL tables with embeddings for semantic search. The speed of databases with the readability of Markdown.
A headless Python daemon that continuously ingests, indexes, and catalogs incoming data - so the primary agent always has organized context.
Run RITA on your own hardware. Desktop apps for Mac, Windows, and Linux make deployment simple. Your data stays yours.
Professor Nova guides skill development. Agents level up through training, share experiences, and continuously improve.
Our Team
Entrepreneur and AI researcher focused on building the next generation of intelligent agent frameworks. Leading the vision for RITA and South Beach Robotics.
AI-powered development partner. Built the RITA platform architecture, memory block system, and Python micro-app framework in collaboration with Rob.
Visit WebsiteResearch Credits
RITA builds upon foundational research in recursive language models and agent memory systems. We gratefully acknowledge the following work that inspired our architecture: