While large language models (LLMs) have exhibited strong capabilities in translating code, particularly from C to Python, their performance noticeably declines when dealing with less common languages like LF. Prior research indicates that Retrieval-Augmented Generation (RAG) can enhance LLMs capabilities in code generation by integrating codebase retrieval. Despite its promise, RAG systems are constrained by LLMs capabilities to deal with less common languages. Agentic AI coding assistants offer a different approach by acting as AI co-developers, automating tedious tasks and allowing developers to focus on high-level design. This paper proposes a novel system that combines RAG with agentic AI assistants to improve the accuracy of converting LF programs with target C into LF code with target Python. We conduct a comparative evaluation of state-of-the-art proprietary and open-source code LLMs in this task, demonstrating that RAG can significantly narrow the performance gap between small and large language models. Furthermore, we integrate an agentic assistant within an AI-powered IDE to automate developer-assisted error correction and refactoring, streamlining the development workflow. In terms of syntax correctness and successful execution rates, experiments highlight the significant improvements achieved by the combined approach.