Multi-agent chatbots are the result of recent advancements in the field of AI and particular developments in large language models (LLMs) and natural language processing (NLP). This research paper explores some of the revolutionary and innovative benefits of the Multi-Agent chatbots, especially those based upon Retrieval-Augmented Generation (RAG) technology. By reviewing frameworks such as RAG, SELF-RAG, and Corrective-RAG (CRAG), our study features progress in improving factual accuracy and reducing hallucinations by using self-reflection and adaptive retrieval. In this paper, we have also discussed the role of some multi-agent frameworks such as AutoGen and LangGraph, which are centered toward task-oriented conversations, in which agents can work on complex tasks with refined output and can validate the information in hand in real-time. The proposed chatbot architecture will have reduced hallucinations, self-reflection, and increased response quality. Finally, the proposed innovative architecture will play a crucial role in building a state-of-the-art chatbot with a wide variety of applications.