[2603.27423] AstraAI: LLMs, Retrieval, and AST-Guided Assistance for HPC Codebases

Computer Science > Artificial Intelligence arXiv:2603.27423 (cs) [Submitted on 28 Mar 2026] Title:AstraAI: LLMs, Retrieval, and AST-Guided Assistance for HPC Codebases Authors:Mahesh Natarajan, Xiaoye Li, Weiqun Zhang View a PDF of the paper titled AstraAI: LLMs, Retrieval, and AST-Guided Assistance for HPC Codebases, by Mahesh Natarajan and 2 other authors View PDF HTML (experimental) Abstract:We present AstraAI, a command-line interface (CLI) coding framework for high-performance computing (HPC) software development. AstraAI operates directly within a Linux terminal and integrates large language models (LLMs) with Retrieval-Augmented Generation (RAG) and Abstract Syntax Tree (AST)-based structural analysis to enable context-aware code generation for complex scientific codebases. The central idea is to construct a high-fidelity prompt that is passed to the LLM for inference. This prompt augments the user request with relevant code snippets retrieved from the underlying framework codebase via RAG and structural context extracted from AST analysis, providing the model with precise information about relevant functions, data structures, and overall code organization. The framework is designed to perform scoped modifications to source code while preserving structural consistency with the surrounding code. AstraAI supports both locally hosted models from Hugging Face and API-based frontier models accessible via the American Science Cloud, enabling flexible deployment across HPC...