This paper addresses the challenge of enabling end users to interact directly with various APIs that provide real-time data and perform actions, eliminating the need for programming or developing intermediate applications. Direct interaction with APIs offers significant benefits, including increased accessibility, reduced development time, and enhanced flexibility in meeting diverse user needs. We propose a chatbot solution that leverages Large Language Models (LLMs) to facilitate these interactions. The advanced language understanding and reasoning capabilities of LLMs underpin our approach, addressing challenges such as precise query refinement, planning the selection of one or more APIs, and extracting parameters from user queries for API input. Our novel architecture integrates question refinement, entity recognition, and API filtering modules, supported by a multi-agent chatbot system that plans and evaluates API usage. This multi-agent system operates as a collaborative team of specialized experts, iteratively handling complex queries. Experimental results demonstrate that our system achieves a 91.95% accuracy rate with minimal response time. This approach simplifies the development of chatbots across various domains by leveraging available APIs, making it easier to build sophisticated, context-aware systems.