Open Code Review

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.

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

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 Design: Deterministic Engineering × Agent Hybrid

Open Code Review's core philosophy is to combine deterministic engineering with an agent, each handling what it does best.

Deterministic Engineering — Hard Constraints

For review steps that must not go wrong, engineering logic — not the language model — guarantees correctness: