# CodeAgent: Autonomous Communicative Agents for Code Review

arXiv: https://arxiv.org/abs/2402.02172 · HTML: https://mlsanity.com/paper/2402.02172/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Placed in

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

## Builds on (most cited first, 27 of 27)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [Large Language Models as Tool Makers](https://mlsanity.com/paper/2305.17126/) (2023)
- [Deep Learning based Vulnerability Detection: Are We There Yet?](https://mlsanity.com/paper/2009.07235/) (2020)
- [MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming](https://mlsanity.com/paper/2310.06500/) (2023)
- [Is ChatGPT the Ultimate Programming Assistant -- How far is it?](https://mlsanity.com/paper/2304.11938/) (2023)
- [Using Pre-Trained Models to Boost Code Review Automation](https://mlsanity.com/paper/2201.06850/) (2022)
- [Towards Automating Code Review Activities](https://mlsanity.com/paper/2101.02518/) (2021)
- [Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models](https://mlsanity.com/paper/2304.13835/) (2023)
- [LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2308.11148/) (2023)
- [Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents](https://mlsanity.com/paper/2310.09343/) (2023)
- [NL-EDIT: Correcting semantic parse errors through natural language interaction](https://mlsanity.com/paper/2103.14540/) (2021)
- [Deep Just-In-Time Inconsistency Detection Between Comments and Source Code](https://mlsanity.com/paper/2010.01625/) (2020)
- [CORE: Automating Review Recommendation for Code Changes](https://mlsanity.com/paper/1912.09652/) (2019)
- [Generation-based Code Review Automation: How Far Are We?](https://mlsanity.com/paper/2303.07221/) (2023)
- [A Systematic Literature Review and Taxonomy of Modern Code Review](https://mlsanity.com/paper/2103.08777/) (2021)
- [Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness](https://mlsanity.com/paper/2208.04125/) (2022)
- [Just-in-Time Detection of Silent Security Patches](https://mlsanity.com/paper/2312.01241/) (2023)
- [Less is More: Supporting Developers in Vulnerability Detection during Code Review](https://mlsanity.com/paper/2202.04586/) (2022)
- [A Systematic Literature Review on the Impact of Formatting Elements on Code Legibility](https://mlsanity.com/paper/2208.12141/) (2022)

## Built on by (most cited first, 30 of 34)

- [A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application](https://mlsanity.com/paper/2412.17481/) (2024)
- [Reinforce LLM Reasoning through Multi-Agent Reflection](https://mlsanity.com/paper/2506.08379/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [An Empirical Study of Agent Developer Practices in AI Agent Frameworks](https://mlsanity.com/paper/2512.01939/) (2025)
- [FinSight: Towards Real-World Financial Deep Research](https://mlsanity.com/paper/2510.16844/) (2025)
- [SimuGen: Multi-modal Agentic Framework for Constructing Block Diagram-Based Simulation Models](https://mlsanity.com/paper/2506.15695/) (2025)
- [CallNavi, A Challenge and Empirical Study on LLM Function Calling and Routing](https://mlsanity.com/paper/2501.05255/) (2025)
- [AgentBay: A Hybrid Interaction Sandbox for Seamless Human-AI Intervention in Agentic Systems](https://mlsanity.com/paper/2512.04367/) (2025)
- [DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning](https://mlsanity.com/paper/2512.07132/) (2025)
- [AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context](https://mlsanity.com/paper/2601.19494/) (2026)
- [SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review](https://mlsanity.com/paper/2607.06065/) (2026)
- [Using LLMs in Generating Design Rationale for Software Architecture Decisions](https://mlsanity.com/paper/2504.20781/) (2025)
- [Issue-Oriented Agent-Based Framework for Automated Review Comment Generation](https://mlsanity.com/paper/2511.00517/) (2025)
- [DRAFT-RL: Multi-Agent Chain-of-Draft Reasoning for Reinforcement Learning-Enhanced LLMs](https://mlsanity.com/paper/2511.20468/) (2025)
- [Code Review Agent Benchmark](https://mlsanity.com/paper/2603.23448/) (2026)
- [When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?](https://mlsanity.com/paper/2510.17862/) (2025)
- [CR-Bench: Evaluating the Real-World Utility of AI Code Review Agents](https://mlsanity.com/paper/2603.11078/) (2026)
- [LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review](https://mlsanity.com/paper/2604.16321/) (2026)
- [Prompt Overflow: What the Guardrail Inspects Is Not What the Model Infers](https://mlsanity.com/paper/2605.23196/) (2026)
- [The End of Code Review: Coding Agents Supersede Human Inspection](https://mlsanity.com/paper/2606.13175/) (2026)
- [AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection](https://mlsanity.com/paper/2601.19138/) (2026)
- [Automated detection of atomicity violations in large-scale systems](https://mlsanity.com/paper/2504.00521/) (2025)
- [Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review](https://mlsanity.com/paper/2605.17548/) (2026)
- [SEVRA-BENCH: Social Engineering of Vulnerabilities in Review Agents](https://mlsanity.com/paper/2606.13757/) (2026)
- [Reformulate, Retrieve, Localize: Agents for Repository-Level Bug Localization](https://mlsanity.com/paper/2512.07022/) (2025)
- [SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning](https://mlsanity.com/paper/2601.21649/) (2026)
- [Assessing Spear-Phishing Website Generation in Large Language Model Coding Agents](https://mlsanity.com/paper/2602.13363/) (2026)
- [XSearch: Explainable Code Search via Concept-to-Code Alignment](https://mlsanity.com/paper/2605.16046/) (2026)
- [AgensFlow: A Coordination-Policy Substrate for Multi-Agent Systems](https://mlsanity.com/paper/2605.27466/) (2026)
- [Converted, Not Equivalent: Benchmarking Codebase Conversion via Observational Equivalence](https://mlsanity.com/paper/2605.29054/) (2026)

## Abstract (arXiv)

Code review, which aims at ensuring the overall quality and reliability of software, is a cornerstone of software development. Unfortunately, while crucial, Code review is a labor-intensive process that the research community is looking to automate. Existing automated methods rely on single input-output generative models and thus generally struggle to emulate the collaborative nature of code review. This work introduces tool, a novel multi-agent Large Language Model (LLM) system for code review automation. CodeAgent incorporates a supervisory agent, QA-Checker, to ensure that all the agents' contributions address the initial review question. We evaluated CodeAgent on critical code review tasks: (1) detect inconsistencies between code changes and commit messages, (2) identify vulnerability introductions, (3) validate code style adherence, and (4) suggest code revision. The results demonstrate CodeAgent's effectiveness, contributing to a new state-of-the-art in code review automation. Our data and code are publicly available (urlhttps://github.com/Code4Agent/codeagent).