Large language models (LLMs) and their derivatives have demonstrated remarkable performance in natural language and downstream tasks. However, their static parameterization and reliance on public data limit real-time adaptability and the integration of private knowledge. Retrieval-augmented generation (RAG) partially addresses these issues by incorporating external knowledge, yet existing approaches primarily rely on public repositories and struggle to handle hybrid knowledge sources. To overcome these limitations, a unified parallel RAG-agent-LLM (PRALLM) framework is proposed in this paper. PRALLM introduces a three-layer knowledge architecture that seamlessly integrates public, private, and personal databases through a unified representation and retrieval mechanism. The framework further incorporates real-time learning (supporting incremental updates), directional learning (user-oriented knowledge acquisition), and personalized private agents (enabling secure domain adaptation). This design enables scalable, adaptive, and privacy-preserving knowledge-enhanced LLMs, effectively bridging the gap between the breadth of public knowledge and the depth of private knowledge in practical applications. Preliminary evaluations indicate that PRALLM is expected to achieve approximately 15%–25% performance improvement across various domain-specific tasks compared with the standard RAG approaches.