In recent years, artificial intelligence systems have predominantly been developed as single, independent agents, which restricts their ability to efficiently handle complex real-world problems that require coordination, collaboration, and parallel execution of tasks. To overcome this limitation, this project presents the design and implementation of an AI Agent Framework for a multi-agent environment, where multiple autonomous agents work together to achieve a common objective. Each agent is assigned a specific role—planning, coding, writing, research, or review—and operates within a shared environment while communicating through a structured orchestration layer to decompose tasks, stream responses in real time, and collaboratively complete assigned goals. The framework, named NexusAI, is implemented using FastAPI (Python) on the backend and React with Vite on the frontend, and leverages the Groq API with the LLaMA-3.3-70B-Versatile model to enable high-speed inference. Experimental evaluation demonstrates that the proposed multi-agent framework enhances efficiency, modularity, and scalability when compared to traditional single-agent systems, effectively supporting automated task execution through parallel agent interactions and Server-Sent Events (SSE) streaming. This project highlights the practical potential of multi-agent AI systems in building intelligent, flexible, and production-deployable solutions, while also providing a strong foundation for future research and development in autonomous agent-based systems.
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Conclusion
This paper presented NexusAI, a modular multi-agent artificial intelligence platform designed to enhance structured reasoning, parallel task execution, and real-time streaming in LLM-based systems. Unlike traditional single-agent architectures, the proposed approach distributes responsibilities across five specialized agents—Planner, Coder, Writer, Researcher, and Reviewer—thereby enabling intelligent task routing, concurrent execution, and coherent output synthesis.
The system integrates the Groq API with the LLaMA-3.3-70B-Versatile model, delivering sub-second first-token latency and high-quality responses without the infrastructure overhead of locally hosted models. Real-time SSE streaming and an in-memory LRU cache provide a responsive, ChatGPT-like user experience deployed publicly on Vercel and Render.
Experimental evaluation demonstrated that the proposed framework improves modularity, interpretability, and extensibility compared to previous methodologies. Parallel agent execution via asyncio.gather() delivered 2–3x speed improvements over sequential approaches, and the conditional Reviewer Agent ensured output quality for complex multi-domain tasks without unnecessary latency on simple queries.
The NexusAI framework establishes a practical bridge between theoretical multi-agent system concepts and real-world full-stack deployment. By combining intelligent task routing, parallel agent orchestration, SSE streaming, SQLite persistence, and LRU caching within a React and FastAPI architecture, the system provides a scalable and accessible foundation for future AI applications.
In summary, the study validates the feasibility and effectiveness of structured multi-agent collaboration in publicly deployed, cloud-native LLM environments, opening new opportunities for modular, production-ready, and cost-efficient AI systems built on modern web technologies.
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