Integration of LLM and ReAct Agents for Enhanced Context Oriented Programming

With the unprecedented development of Large Language Models (LLM), Context-Oriented Programming (COP) is becoming increasingly important for a wide range of software development as well as business applications. Unfortunately, traditional programming languages do not provide mechanisms that enable software units to adapt their behavior dynamically based on input context. This leads software developers to adopt intricate designs to achieve the necessary runtime flexibility. Recently, researchers have utilized LLMs along with agents and tools to perform a wide range of software development tasks, which have demonstrated promising results. However, these approaches are still limited to a few areas of software eco-system. In this research, we propose the utilization of LLMs in conjunction with ReAct agents to enhance the overall adaptability of LLMs in selecting appropriate tools based on consistent contextual data. By leveraging the ReAct agent as a mediator between the LLM and the tools, we aim to offer a more streamlined and efficient approach to COP. The proposed system optimally utilizes context to determine the operational tools required, thus facilitating efficient and effective responses for complex query scenarios.