Open Code Review is an AI-powered command-line interface (CLI) tool designed for automated code reviews. It originated as Alibaba Group's internal AI code review assistant and has since evolved into an open-source project after serving tens of thousands of developers and identifying millions of code defects.
Key Features:
Reads Git diffs and analyzes changed files using a configurable large language model (LLM).
Generates structured, line-level review comments with precision.
Employs an agent with tool-use capabilities to inspect full file contents, search the codebase, and produce deep reviews beyond surface-level feedback.
Supports audits of entire codebases or directories via the ocr scan command for unfamiliar projects.
Audience & Benefit:
Ideal for developers, engineering teams, and organizations managing large-scale codebases. Open Code Review improves code quality by identifying potential defects early in the development cycle. Its precise analysis reduces false positives, while its ability to handle extensive changesets ensures comprehensive coverage of reviews. It can be installed via winget, making it accessible for seamless integration into existing workflows.
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.
> 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).
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.
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:
Precise file selection — Determines exactly which files need review and which should be filtered, ensuring no important change is missed.
Smart file bundling — Groups related files into a single review unit (e.g., message_en.properties and message_zh.properties are bundled together). Each bundle runs as a sub-agent with isolated context — a divide-and-conquer strategy that stays stable on very large changesets and naturally supports concurrent review.
Fine-grained rule matching — Matches review rules to each file's characteristics, keeping the model's attention sharply focused and eliminating information noise at the source. Compared to purely language-driven rule guidance, template-engine-based rule matching is more stable and predictable.
External positioning and reflection modules — Independent comment-positioning and comment-reflection modules systematically improve both the location accuracy and content accuracy of AI feedback.
Agent — Dynamic Decision-Making
The agent's strengths are concentrated where they matter most — dynamic decisions and dynamic context retrieval:
Scenario-tuned prompts — Prompt templates deeply optimized for code review, improving effectiveness while reducing token consumption.
Scenario-tuned toolset — Distilled from deep analysis of tool-call traces in large-scale production data — including call frequency distributions, per-tool repetition rates, and the impact of new tools on the overall call chain — resulting in a purpose-built toolset that is more stable and predictable for code review than a generic agent toolkit.
How to Use
Prerequisites
Git >= 2.41 — Open Code Review relies on Git for diff generation, code search, and repository operations.
CLI
Install
npm install -g @alibaba-group/open-code-review
After installation, the ocr command is available globally.
For other installation methods (install script, GitHub Release binary, from source), see Installation.
Quick Start
1. Configure LLM
You must configure an LLM before reviewing code, unless you use Delegation Mode.
ocr config provider # Select a built-in provider or add a custom one
ocr config model # Pick a model for the active provider
The interactive UI guides you through provider selection, API key entry, and model configuration, then automatically tests connectivity.
For CLI setup, environment variables, custom providers, and other advanced configuration, see Configuration.
2. Review
cd your-project
# Workspace mode — review all staged, unstaged, and untracked changes
ocr review
# Branch range — compare two refs
ocr review --from main --to feature-branch
# Single commit
ocr review --commit abc123
# Resume an interrupted range or commit review
ocr session list
ocr review --from main --to feature-branch --resume
# Print the review comments recorded in a saved session
ocr session comments
ocr session comments --severity critical,high --json
# Full-file scan — review whole files instead of a diff (no git history needed)
ocr scan # scan the entire repository
ocr scan --path internal/agent # scan a directory or specific files
ocr scan --resume # resume an interrupted full-file scan
# Delegation mode — let your AI coding agent perform the review itself
# OCR handles file selection and rule resolution; no LLM configuration needed
ocr delegate preview
ocr delegate rule src/main.go src/handler.go
This project exists thanks to all the people who contribute. See CONTRIBUTING.md for development setup, coding guidelines, and how to submit pull requests.
