OmniCore AI – Multi-Agent System

Abstract

The rapid growth of artificial intelligence has led to significant advancements in multi-modal learning, where systems are designed to understand and process information from multiple heterogeneous data sources such as text, images, audio, and video. However, building a unified framework capable of intelligently routing user inputs to specialized AI models, coordinating their outputs, and delivering meaningful results in real time remains a major technical challenge. This project presents OmniCore AI, a scalable and modular multi-modal AI agent system designed to integrate text, image, audio, and video processing capabilities into a single unified architecture using independent microservice-based agents.
OmniCore AI employs a central routing engine built with FastAPI that performs intent detection, modality classification, and task routing. When a user provides input—whether text, an image, an audio recording, or a video clip—the router identifies the data type and forwards it to the appropriate specialized agent. Each agent runs as an independent Dockerized microservice, enabling asynchronous processing, fault isolation, and seamless scalability.
Overall, OmniCore AI demonstrates a robust, efficient, and extensible approach to combining multiple AI models into a single cohesive system. By integrating text, vision, audio, and video intelligence using independent agents and centralized routing, this project showcases a next-generation architecture for multi-modal understanding. The modularity, scalability, and flexibility of the system position it as a foundation for advanced AI research and practical, industry-grade deployments. This work highlights the potential of multi-agent multi-modal architectures in shaping the future of intelligent systems.