We present Code Broker, a multi agent system built with Google Agent Development Kit ADK that analyses Python code from files, local directories, or GitHub repositories and generates actionable quality assessment reports. The system employs a hierarchical five agents architecture in which a root orchestrator coordinates a sequential pipeline agent, which in turn dispatches three specialised agents in parallel a Correctness Assessor, a Style Assessor, and a Description Generator before synthesising findings through an Improvement Recommender. Reports score four dimensions correctness, security, style, and maintainability and are rendered in both Markdown and HTML. Code Broker combines LLM based reasoning with deterministic static-analysis signals from Pylint, uses asynchronous execution with retry logic to improve robustness, and explores lightweight session memory for retaining and querying prior assessment context. We position the paper as a technical report on system design and prompt or tool orchestration, and present a preliminary qualitative evaluation on representative Python codebases. The results suggest that parallel specialised agents produce readable, developer oriented feedback, while also highlighting current limitations in evaluation depth, security tooling, large repository handling, and the current use of only in memory persistence. All code and reproducibility materials are available at: https://github.com/Samir-atra/agents_intensive_dev.

翻译：我们提出了Code Broker，这是一个基于Google Agent Development Kit ADK构建的多智能体系统，能够分析来自文件、本地目录或GitHub仓库的Python代码，并生成可操作的代码质量评估报告。该系统采用五层分级智能体架构：根协调器负责调度顺序流水线智能体，该智能体随后并行分派三个专业智能体——正确性评估器、风格评估器和描述生成器，最后由改进建议器综合所有分析结果。报告从正确性、安全性、风格和可维护性四个维度进行评分，并同时生成Markdown和HTML格式。Code Broker将基于大语言模型的推理能力与来自Pylint的确定性静态分析信号相结合，采用带有重试机制的异步执行以提高鲁棒性，并探索了轻量级会话记忆以保留和查询历史评估上下文。本文作为一份技术报告，重点阐述系统设计以及提示词与工具编排方案，并在典型Python代码库上进行了初步定性评估。结果表明，并行专精智能体能够生成可读性强、面向开发者的反馈，同时也揭示了当前在评估深度、安全工具、大型仓库处理以及仅使用内存持久化等方面的局限性。所有代码及可复现性材料均可在以下地址获取：https://github.com/Samir-atra/agents_intensive_dev