# AstraAI: LLMs, Retrieval, and AST-Guided Assistance for HPC Codebases

arXiv: https://arxiv.org/abs/2603.27423 · HTML: https://mlsanity.com/paper/2603.27423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Builds on (most cited first, 13 of 13)

- [Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation](https://mlsanity.com/paper/2309.07103/) (2023)
- [Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation](https://mlsanity.com/paper/2306.15121/) (2023)
- [HPC-GPT: Integrating Large Language Model for High-Performance Computing](https://mlsanity.com/paper/2311.12833/) (2023)
- [The Landscape and Challenges of HPC Research and LLMs](https://mlsanity.com/paper/2402.02018/) (2024)
- [LM4HPC: Towards Effective Language Model Application in High-Performance Computing](https://mlsanity.com/paper/2306.14979/) (2023)
- [Scope is all you need: Transforming LLMs for HPC Code](https://mlsanity.com/paper/2308.09440/) (2023)
- [Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++](https://mlsanity.com/paper/2307.07686/) (2023)
- [HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights](https://mlsanity.com/paper/2505.04846/) (2025)
- [LLM & HPC:Benchmarking DeepSeek's Performance in High-Performance Computing Tasks](https://mlsanity.com/paper/2504.03665/) (2025)
- [AMReX: Block-Structured Adaptive Mesh Refinement for Multiphysics Applications](https://mlsanity.com/paper/2009.12009/) (2020)
- [LLM as HPC Expert: Extending RAG Architecture for HPC Data](https://mlsanity.com/paper/2501.14733/) (2024)
- [Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions](https://mlsanity.com/paper/2505.08135/) (2025)
- [LLMs as Packagers of HPC Software](https://mlsanity.com/paper/2511.05626/) (2025)

## Abstract (arXiv)

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 environments. The system generates code that aligns with existing project structures and programming patterns. We demonstrate AstraAI on representative HPC code generation tasks within AMReX, a DOE-supported HPC software infrastructure for exascale applications.