Retrieval Augmented Generation (RAG) has emerged as a powerful paradigm for improving the accuracy and trustworthiness of large language models (LLMs) by grounding responses in external knowledge sources. However, existing implementations often depend on cloud-based infrastructures, raising concerns around privacy, latency, and reliability. This paper introduces Local Agentic RAG, a framework that integrates retrieval-augmented pipelines with locally deployed agentic reasoning modules to enable context-aware, privacy-preserving, and adaptive generation. Unlike traditional RAG, which primarily focuses on static retrieval, Local Agentic RAG leverages agent-driven orchestration to dynamically select retrieval strategies, assess knowledge gaps, and refine outputs through iterative reasoning. Experimental evaluations highlight the advantages of this approach in terms of reduced hallucination rates, enhanced factual accuracy, and improved response adaptability, while maintaining data sovereignty. Furthermore, we provide a comparative analysis of Local Agentic RAG across two leading LLM ecosystems—OpenAI’s GPT models and Meta’s LLaMA family—demonstrating that the proposed framework can be flexibly deployed across diverse architectures. Our findings indicate that Local Agentic RAG offers a scalable and secure pathway toward domain-specific, trustworthy AI applications in healthcare, finance, and enterprise knowledge management.