# Code Broker: A Multi-Agent System for Automated Code Quality Assessment

arXiv: https://arxiv.org/abs/2604.23088 · HTML: https://mlsanity.com/paper/2604.23088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 44 references that are in the map.
- First posted on arXiv 25 Apr 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 24 of 24)

- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering](https://mlsanity.com/paper/2604.08224/) (2026)
- [HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale](https://mlsanity.com/paper/2409.16299/) (2024)
- [ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems](https://mlsanity.com/paper/2408.02248/) (2024)
- [LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLM](https://mlsanity.com/paper/2512.01356/) (2025)
- [LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities](https://mlsanity.com/paper/2601.09822/) (2026)
- [Facilitating Trustworthy Human-Agent Collaboration in LLM-based Multi-Agent System oriented Software Engineering](https://mlsanity.com/paper/2505.04251/) (2025)
- [What Types of Code Review Comments Do Developers Most Frequently Resolve?](https://mlsanity.com/paper/2510.05450/) (2025)
- [HiveMind: Contribution-Guided Online Prompt Optimization of LLM Multi-Agent Systems](https://mlsanity.com/paper/2512.06432/) (2025)
- [Agyn: A Multi-Agent System for Team-Based Autonomous Software Engineering](https://mlsanity.com/paper/2602.01465/) (2026)
- [Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering](https://mlsanity.com/paper/2505.03096/) (2025)
- [Bridging the Prototype-Production Gap: A Multi-Agent System for Notebooks Transformation](https://mlsanity.com/paper/2511.07257/) (2025)
- [Code Review Agent Benchmark](https://mlsanity.com/paper/2603.23448/) (2026)
- [Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale](https://mlsanity.com/paper/2511.08475/) (2025)
- [An Architectural Style for Self-Adaptive Multi-Agent Systems](https://mlsanity.com/paper/1909.03475/) (2019)
- [RV4JaCa -- Runtime Verification for Multi-Agent Systems](https://mlsanity.com/paper/2207.09708/) (2022)
- [Towards autonomous normative multi-agent systems for Human-AI software engineering teams](https://mlsanity.com/paper/2512.02329/) (2025)
- [Analyzing Code Injection Attacks on LLM-based Multi-Agent Systems in Software Development](https://mlsanity.com/paper/2512.21818/) (2025)
- [Towards Jacamo-rest: A Resource-Oriented Abstraction for Managing Multi-Agent Systems](https://mlsanity.com/paper/2006.05619/) (2020)
- [Multi-Agent Systems for Dataset Adaptation in Software Engineering: Capabilities, Limitations, and Future Directions](https://mlsanity.com/paper/2511.21380/) (2025)
- [When Code Becomes Abundant: Redefining Software Engineering Around Orchestration and Verification](https://mlsanity.com/paper/2602.04830/) (2026)
- [Loosely-Structured Software: Engineering Context, Structure, and Evolution Entropy in Runtime-Rewired Multi-Agent Systems](https://mlsanity.com/paper/2603.15690/) (2026)

## Abstract (arXiv)

We present Code Broker, a multi agent system built on Google s Agent Development Kit ADK that analyses Python source code from individual files, local directory trees, or remote GitHub repositories and generates structured, actionable quality assessment reports. The system realises a hierarchical five agent architecture in which a root orchestrator coordinates a sequential pipeline agent that, in turn, dispatches three specialised agents concurrently a Correctness Assessor, a Style Assessor, and a Description Generator before synthesising their findings through an Improvement Recommender. Reports quantify four quality dimensions correctness, security, style, and maintainability on a normalised scale and are rendered in both Markdown and HTML for integration into diverse developer workflows. Code Broker fuses LLM based semantic reasoning with deterministic static analysis signals from Pylint, employs asynchronous execution with exponential backoff retry logic to improve robustness under transient API failures, and explores lightweight session memory for retaining and querying prior assessment context across runs. We frame this paper as a technical report on system design, prompt engineering, and tool orchestration, and present a preliminary qualitative evaluation on representative Python codebases of varying scale. The results indicate that parallel specialised agents produce readable, developer oriented feedback that complements traditional linting, while also foregrounding current limitations in evaluation depth, security tooling, large repository handling, and the exclusive reliance on in memory persistence. All code and reproducibility materials are publicly available: https://github.com/Samir-atra/agents_intensive_dev.