Real-Time Adaptive Code Analysis with a Self-Learning Multi-Agent Framework: A Retrieval-Augmented Reinforcement Learning Approach

Large Language Models (LLMs) have transformed code generation, debugging, and security analysis, yet their application in real-time, comprehensive code review remains under explored. This paper introduces a novel real-time multiagent framework that leverages adaptive reinforcement learning, transformer-based code embeddings, and FAISSpowered memory to deliver dynamic, context-aware code reviews within a VS Code extension. By distributing tasks among specialized agents for code review, bug detection, security analysis, and performance optimization, the system offers immediate and explainable feedback that evolves with individual developer practices. Experimental evaluations on Python code snippets demonstrate that our approach enhances issue resolution speed, minimizes redundant feedback, and fosters developer trust through transparent, actionable insights. The modular architecture and robust inter-agent collaboration facilitate seamless integration into modern development workflows, including IDE plugins and CI/CD pipelines. This work advances the state-of-the-art in AIdriven software engineering and paves the way for scalable, adaptive, and secure code analysis.