标题： 基于代理的Gammapy框架代码生成 显示英文标题

标题： Agent-based code generation for the Gammapy framework

摘要： 使用大型语言模型（LLMs）进行软件代码生成是现代人工智能最成功的应用之一。 基础模型对于受益于文档、示例和强大社区支持的流行框架非常有效。 相比之下，专业的科学库通常缺乏这些资源，并可能在积极开发中暴露不稳定的API，这使得在有限或过时数据上训练的模型难以应对。 我们通过开发一个能够在受控环境中编写、执行和验证代码的代理来解决Gammapy库中的这些问题。 我们展示了一个最小的网络演示和一个配套的基准测试套件。 本贡献总结了设计，报告了我们的当前状态，并概述了下一步计划。 显示英文摘要

摘要： Software code generation using Large Language Models (LLMs) is one of the most successful applications of modern artificial intelligence. Foundational models are very effective for popular frameworks that benefit from documentation, examples, and strong community support. In contrast, specialized scientific libraries often lack these resources and may expose unstable APIs under active development, making it difficult for models trained on limited or outdated data. We address these issues for the Gammapy library by developing an agent capable of writing, executing, and validating code in a controlled environment. We present a minimal web demo and an accompanying benchmarking suite. This contribution summarizes the design, reports our current status, and outlines next steps.