Large Language Models (LLMs) have been at the forefront of applied Artificial Intelligence (AI) research in a multitude of domains. One such domain is software development, where researchers have pushed the automation of a number of code tasks through LLM agents. In this paper, we propose a system to localize bugs in large pre-existing codebases using information retrieval and LLMs. Our system introduces a novel Retrieval Augmented Generation (RAG) approach, Meta-RAG, where we utilize summaries to condense codebases by an average of 79.8%, into a compact, structured, natural language representation. We then use a separate LLM agent to determine which parts of the codebase are critical for bug resolution, i.e. bug localisation. We demonstrate the usefulness of Meta-RAG through evaluation with the SWE-bench Lite dataset. Meta-RAG scores 84.67% and 53.0% for file-level and function-level correct localisation rates, respectively, achieving state-of-the-art performance.