README

OpenCodeReview



  
    
  


  
    
  
  
    
  


  
  
  
  
  


  
  
  
  
  
  
  


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 What is Open Code Review?

Open Code Review is an AI-powered code review CLI tool. It originated as Alibaba Group's internal official AI code review assistant — over the past two years, it has served tens of thousands of developers and identified millions of code defects. After thorough validation at massive scale, we incubated it into an open source project for the community. Simply configure a model endpoint to get started.

It reads Git diffs, sends changed files to a configurable LLM via an agent with tool-use capabilities, and generates structured review comments with line-level precision. The agent can read full file contents, search the codebase, inspect other changed files for context, and produce deep reviews — not just surface-level diff feedback. Beyond diff review, ocr scan reviews entire files for auditing unfamiliar codebases or directories that have no meaningful diff.

Visit the official website for more details.

!Highlights

 Benchmark

 Compared to general-purpose agents (Claude Code), Open Code Review achieves significantly higher Precision and F1 with the same underlying model, while consuming only ~1/9 of the tokens and completing reviews faster. Note that its Recall is lower than general-purpose agents — a deliberate trade-off favoring precision over noise.

A real-world code review benchmark built from 50 popular open-source repositories, 200 real Pull Requests, and 10 programming languages — cross-validated by 80+ senior engineers (1,505 annotated ground-truth issues).

 Explore the AACR-Bench dataset on Hugging Face.

| Metric | What it measures | Why it matters |
|--------|-----------------|----------------|
| F1 | Harmonic mean of precision and recall | Best single number for overall review quality |
| Precision | Proportion of reported issues that are real defects | Higher = fewer false alarms to triage |
| Recall | Proportion of real defects that are found | Higher = fewer issues slip through review |
| Avg Time | Wall-clock time per review | Matters for CI pipeline latency |
| Avg Token | Total tokens consumed per review | Directly impacts API cost |

!Benchmark

 Why Open Code Review?

 The Problem with General-Purpose Agents

If you've used general-purpose agents like Claude Code with Skills for code review, you've likely encountered these pain points:

- Incomplete coverage — On larger changesets, agents tend to "cut corners," selectively reviewing only some files and missing others.
- Position drift — Reported issues frequently don't match the actual code location, with line numbers or file references drifting off target.
- Unstable quality — Natural-language-driven Skills are hard to debug, and review quality fluctuates significantly with minor prompt variations.

The root cause: a purely language-driven architecture lacks hard constraints on the review process.

 Core De

Top issues

Discussions

Latest release

🚀 Features

- feat(pages): showcase supported model providers (1862)
- feat(lang): review Starlark files (1851)
- feat(rules): add C review rules (1840)

 🐛 Bug Fixes

- fix(llm): preserve explicit false stream override (1531)
- fix(action): only rewrite the bot's own summary comment (1560)


Full Changelog: https://github.com/alibaba/open-code-review/compare/v1.13.5...v1.13.6