ERAGent is a novel framework that enhances Retrieval-Augmented Generation (RAG) by improving accuracy, efficiency, and personalization. It introduces an Enhanced Question Rewriter and Knowledge Filter module to improve retrieval quality, a Retrieval Trigger to avoid unnecessary external knowledge retrieval, and a Personalized LLM Reader to tailor responses to individual users. The Experiential Learner module enables the AI assistant to learn from past interactions, improving knowledge retrieval and user profile understanding over time. ERAGent has been rigorously evaluated across six datasets and three question-answering tasks, demonstrating superior accuracy, efficiency, and personalization. The framework's modular design allows for flexibility and adaptability, making it suitable for various applications. ERAGent's key components include an Enhanced Question Rewriter that refines user questions for better retrieval, a Retrieval Trigger that selectively engages external knowledge retrieval, a Knowledge Filter that removes irrelevant information, and a Personalized LLM Reader that generates tailored responses. The Experiential Learner module continuously updates the AI assistant's knowledge base and user profile, enhancing the system's ability to provide accurate and personalized responses. ERAGent's effectiveness is supported by experiments showing improved performance in open-domain and multi-hop reasoning tasks, as well as in multi-session, multi-round conversations where personalized responses significantly outperform non-personalized ones. The framework's efficiency is also demonstrated by its ability to reduce response time without compromising accuracy, particularly when leveraging experiential knowledge. Overall, ERAGent represents a significant advancement in RAG technology, offering a robust solution for improving the accuracy, efficiency, and personalization of AI assistants.ERAGent is a novel framework that enhances Retrieval-Augmented Generation (RAG) by improving accuracy, efficiency, and personalization. It introduces an Enhanced Question Rewriter and Knowledge Filter module to improve retrieval quality, a Retrieval Trigger to avoid unnecessary external knowledge retrieval, and a Personalized LLM Reader to tailor responses to individual users. The Experiential Learner module enables the AI assistant to learn from past interactions, improving knowledge retrieval and user profile understanding over time. ERAGent has been rigorously evaluated across six datasets and three question-answering tasks, demonstrating superior accuracy, efficiency, and personalization. The framework's modular design allows for flexibility and adaptability, making it suitable for various applications. ERAGent's key components include an Enhanced Question Rewriter that refines user questions for better retrieval, a Retrieval Trigger that selectively engages external knowledge retrieval, a Knowledge Filter that removes irrelevant information, and a Personalized LLM Reader that generates tailored responses. The Experiential Learner module continuously updates the AI assistant's knowledge base and user profile, enhancing the system's ability to provide accurate and personalized responses. ERAGent's effectiveness is supported by experiments showing improved performance in open-domain and multi-hop reasoning tasks, as well as in multi-session, multi-round conversations where personalized responses significantly outperform non-personalized ones. The framework's efficiency is also demonstrated by its ability to reduce response time without compromising accuracy, particularly when leveraging experiential knowledge. Overall, ERAGent represents a significant advancement in RAG technology, offering a robust solution for improving the accuracy, efficiency, and personalization of AI assistants.