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ReviewChain

An End-to-End, Feedback-Driven LLM System for Automated Code Review

ReviewChain is a lightweight command-line tool that automates code review using large language models (LLMs). Unlike existing tools that perform review tasks in isolation, ReviewChain models code review as an iterative, multi-stage pipeline, enabling comments, refinements, and quality assessment to inform each other through explicit feedback.

ReviewChain is not a code generation assistant. Instead, it targets a largely underserved part of the development workflow: iterative code review of existing changes. It focuses on helping developers review, refine, and improve code after it is written, closely mirroring real-world human review practices.

diagram

Why ReviewChain?

Code review is essential but time-consuming. Developers often spend significant effort writing review comments, revising code, and validating changes, especially for small or repetitive issues.

Target users:

  • Software developers

  • Open-source contributors

  • Teams seeking faster, more consistent code reviews

  • Researchers exploring multi-agent LLM systems for software engineering

The main advantages of Review Chain over similar tools include:

  • Free and open-source usage.

  • Lightweight design, using small, task-specific LLMs with reasonable computational requirements.

  • Customizable, allowing modification and adaptation for specific projects or workflows.

  • Secure for personal projects, with no concerns about data leakage due to its open-source nature.

  • Easy Git integration, connecting to GitHub through system credentials without additional configuration steps.

Key Features

🔁 Iterative Review Pipeline

ReviewChain decomposes code review into four structured stages:

  1. Review Comment Generation: identifies issues and suggestions

  2. Comment Format Validation: ensures comments are clear and actionable

  3. Code Refinement: applies feedback to improve the code

  4. Quality Estimation: decides whether further refinement is needed

These stages are connected through an explicit feedback loop, allowing the system to improve outputs across multiple rounds.

🧩 Modular LLM Components

Each stage is handled by a specialized LLM, making the system:

  • Easier to extend or replace components

  • More interpretable than monolithic approaches

  • Well-suited for experimentation and research

💻 Simple Command-Line Interface

ReviewChain runs locally and integrates easily with Git repositories. A single command triggers the full review pipeline.

user_experience

Installation & Setup

After cloning the repository, run the following command inside your virtual environment to install the package:

pip install .

You can run ReviewChain using the following command:

reviewchain --path "path/to/target/file" --branch "target-branch-name"

Attention: The target file must already exist in your Git repository.

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