The exponential expansion of biomedical publications has created a persistent challenge of information overloadfor clinicians,researchers, and policy-makers. Manual review and synthesis of medical literature are increasingly impractical, while current automatedsummarization systems often suffer from hallucinations, limited factual grounding, and dependence on external cloudservicesthatcompromise data privacy and reproducibility.This paper presents an Agent-Based Reliable Retrieval-Augmented Generation(RAG)Framework designed to generate concise, evidence-grounded, and verifiable summaries of biomedical literature. The proposedsystemintegrates multiple coordinated agents—Retriever, Summarizer, Fact-Checker, Citation Manager, and Reliability Evaluator—toensurethat each generated summary maintains factual accuracy and transparent citation linkage. Operating entirely in an offlineenvironment,the framework preserves user privacy and supports reproducibility on standard academic hardware.Evaluation will employbenchmarkbiomedical datasets such as PubMed and BioASQ, with both lexical and faithfulness-oriented metrics, including ROUGE, BLEU,evidence-coverage ratio, hallucination rate, and citation accuracy. The framework aims to bridge the reliability gapbetweenlargelanguage models and the stringent requirements of healthcare informatics, offering a trustworthy, reproducible, and ethicallycompliantsolution for automated biomedical knowledge synthesis. Keywords: Biomedical Literature Summarization; Retrieval-Augmented Generation (RAG); Agent-Based Framework; FaithfulnessandReliability in NLP; Medical Informatics; Healthcare Artificial Intelligence; Of line Deployment; Evidence Attribution.