In today’s world of information overload, researchers face increased difficulty in identifying reliable, verified, and up-to-date knowledge from vast online data sources. While Large Language Models (LLMs) and traditional Retrieval-Augmented Generation (RAG) systems support factual retrieval, they remain limited by single-agent reasoning, lack of integrated validation, and dependence on static or outdated knowledge bases. This paper proposes DeepRAG, a conceptual, implementation-ready multi-agent Retrieval-Augmented Generation framework designed to support autonomous and verifiable research synthesis. DeepRAG introduces a coordinated set of specialised agents for planning, semantic retrieval, real-time web data acquisition, citation validation, knowledge synthesis, and structured report generation. The framework leverages agentic orchestration mechanisms (e.g., Autogen), vector-based retrieval using ChromaDB, and real-time web crawling via Crawl4AI to address core limitations of existing RAG pipelines. Rather than presenting empirical benchmarks, this work focuses on architectural design, agent-level responsibilities, and workflow specification, demonstrating how DeepRAG is intended to generate citation-backed research reports in PDF format using the FPDF library. Design-level reasoning indicates that the proposed framework addresses challenges related to factual grounding, citation consistency, and transparency in AI-assisted research. DeepRAG thus provides a robust foundation for future prototype development, experimental evaluation, and deployment of autonomous research assistants.