Open Code Review

Overview

Open Code Review (OCR) is an AI-powered CLI code review tool originally developed as Alibaba Group's internal AI code review assistant. Over two years of internal use, it has served tens of thousands of developers and identified millions of code defects — making it one of the most battle-tested AI code review tools available.

Released as open-source (Apache-2.0) in mid-2026, Open Code Review brings enterprise-scale code review capabilities to any development team. With 13,200+ GitHub stars, it has rapidly become one of the most popular AI code review tools on GitHub.

What makes Open Code Review distinctive is its hybrid architecture: deterministic rule pipelines combined with LLM Agent capabilities. This pragmatic middle ground provides the precision of rule-based linters with the contextual understanding of large language models — catching both known vulnerability patterns and novel issues that rules alone would miss.

Features

• Hybrid Architecture: Deterministic rule pipelines + LLM Agent for comprehensive coverage
• Precise Line-Level Comments: Accurate issue positioning down to the specific line and column
• Built-in Fine-Tuned Ruleset: Pre-trained rules covering NPE, thread-safety, XSS, SQL injection, and more
• Model Agnostic: Supports OpenAI, Anthropic, and any compatible LLM provider
• Full-File Scanning: Review entire files for auditing unfamiliar codebases
• Delegation Mode: AI coding agents can perform reviews using their own LLM
• Branch Comparison: Review differences between any two branches
• Commit-level Review: Review specific commits in isolation
• Cross-platform: Windows, macOS, and Linux support
• Agent Compatible: Works with Claude Code, Codex, Cursor, and other coding agents

Installation

# Global install
npm install -g @alibaba-group/open-code-review

After installation, the ocr command becomes globally available:

ocr --version

Quick Start

# 1. Configure your LLM provider and model
ocr config provider    # Select provider (OpenAI, Anthropic, etc.)
ocr config model       # Select model (e.g., gpt-4, claude-sonnet)

# 2. Navigate to your project
cd your-project

# 3. Review staged and unstaged changes
ocr review

# 4. Compare two branches
ocr review --from main --to feature-branch

# 5. Review a specific commit
ocr review --commit abc123

# 6. Full-file scan (useful for auditing unfamiliar codebases)
ocr scan

Core Concepts

Hybrid Architecture

Open Code Review uses a two-stage review process:

1. Deterministic Pipeline: Fast, rule-based scanning for known vulnerability patterns (NPE, XSS, SQL injection, thread-safety issues). This catches high-confidence issues quickly and reliably.

2. LLM Agent Review: Semantic analysis of the full change context using a large language model. This catches novel issues, architectural concerns, and style problems that rules alone would miss.

Fine-Tuned Ruleset

The built-in ruleset was trained on millions of real code defects identified at Alibaba. This gives OCR a significant advantage over generic linters — the rules reflect actual production incidents, not theoretical vulnerabilities.

Delegation Mode

In delegation mode, an AI coding agent can perform code reviews using its own LLM — enabling agent self-review and reducing the need for a separate review tool.

Advanced Features

Custom Rule Configuration

# Create a custom ruleset
ocr rules create my-ruleset

# Add a custom rule
ocr rules add my-ruleset --pattern "TODO|FIXME" --severity warning

# Use custom ruleset for review
ocr review --ruleset my-ruleset

Multi-Provider Support

# Configure multiple providers
ocr config provider openai
ocr config provider anthropic

# Switch providers per review
ocr review --provider openai
ocr review --provider anthropic

Integration with CI/CD

# Review in CI pipeline
ocr review --from origin/main --to $GITHUB_SHA --format json

# Fail build on critical issues
ocr review --threshold critical

Examples

Basic Code Review

# Review all changes in current branch
cd my-project
git checkout feature/auth
ocr review --from main --to feature/auth

Output:

Reviewing feature/auth against main...

File: src/auth/login.js:42
Severity: HIGH
Issue: Potential SQL injection - unsanitized user input
Fix: Use parameterized query: db.query("SELECT * FROM users WHERE email = ?", [email])

File: src/auth/session.js:18
Severity: MEDIUM
Issue: Thread-safety concern - shared state without synchronization
Fix: Use a mutex or atomic operations for shared session state

CI Integration (GitHub Actions)

- name: AI Code Review
  run: |
    npm install -g @alibaba-group/open-code-review
    ocr config provider openai
    ocr config model gpt-4o
    ocr review --from origin/main --to ${{ github.sha }} --format json --output review.json

Pros

• ✅ Battle-tested at Alibaba scale — Served tens of thousands of developers
• ✅ Hybrid architecture — Best of rule-based and LLM-based review
• ✅ Model agnostic — Works with any OpenAI/Anthropic-compatible provider
• ✅ Cross-platform — Windows, macOS, Linux
• ✅ Agent compatible — Works with Claude Code, Codex, Cursor
• ✅ Delegation mode — Agents can self-review with their own LLM
• ✅ Open-source — Apache-2.0 license, community contributions welcome

Cons

• ❌ LLM-dependent for depth — Requires paid LLM API for full capabilities
• ❌ New project — Community and ecosystem still growing
• ❌ Node.js required — npm install needed for global setup

When to Use

Open Code Review is ideal for:

• Enterprise teams who need production-grade code review at scale
• Security-focused teams who want both rule-based and AI-based vulnerability detection
• Multi-language teams who need model-agnostic review (OpenAI, Anthropic, or custom models)
• CI/CD pipelines that need automated, high-quality code review
• Teams using AI coding agents who want agent-compatible review tools

Open Code Review is less suitable for:

• Teams that want a purely offline, no-API code review solution
• Projects that prefer in-editor review over CLI-based workflows

Resources

• Official GitHub
• Documentation
• npm Package