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open-code-review

Open Code Review is an AI-powered code review CLI tool.

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来源文件:README.md

抓取于 2026年8月1日
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OpenCodeReview

alibaba%2Fopen-code-review | Trendshift alibaba%2Fopen-code-review | Trendshift

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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).

MetricWhat it measuresWhy it matters
F1Harmonic mean of precision and recallBest single number for overall review quality
PrecisionProportion of reported issues that are real defectsHigher = fewer false alarms to triage
RecallProportion of real defects that are foundHigher = fewer issues slip through review
Avg TimeWall-clock time per reviewMatters for CI pipeline latency
Avg TokenTotal tokens consumed per reviewDirectly 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 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:

  • 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

Provider setup

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 <session-id>

# 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

# 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

Documentation

Full documentation lives at open-codereview.ai/docs:

  • Quickstart — install and run your first review
  • Installation — all platforms and package managers
  • CLI Reference — every command and flag
  • Review Rules — customize review rules with path filtering and targeting
  • Configuration — config keys and environment variables
  • MCP Server — extend the review agent with external tools
  • Coding Agent Integrations — choose the platform you use
    • Claude Code — install a plugin with review slash commands
    • Codex — install a plugin with callable review skills
    • Cursor — install a plugin with portable review skills
    • OpenCode — install native review tools and slash commands
    • Skill-compatible agents — install the portable agent skill
  • Review Execution Modes — after integration, choose which LLM performs the review
  • CI/CD Integration — GitHub Actions, GitLab CI, GitFlic CI, and Gerrit integration
  • Session Viewer — browse and replay review sessions in browser
  • Telemetry — OpenTelemetry integration for observability
  • FAQ — common questions and troubleshooting

Contributing

This project exists thanks to all the people who contribute. See CONTRIBUTING.md for development setup, coding guidelines, and how to submit pull requests.

License

Apache-2.0 — Copyright 2026 Alibaba

开发与工程

中风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 可能需要外部 token、网络权限或第三方服务。
  • 未检测到高风险命令。
  • 扫描发现:3 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/alibaba/open-code-review.git
  3. 将 "skills/open-code-review" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/alibaba/open-code-review.git
  3. 将 "skills/open-code-review" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/alibaba/open-code-review.git
  3. 将 "skills/open-code-review" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/alibaba/open-code-review.git
  3. 将 "skills/open-code-review" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/alibaba/open-code-review.git
  3. 将 "skills/open-code-review" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: open-code-review
description: >
  Performs AI-powered code review on Git changes using the `ocr` CLI from
  alibaba/open-code-review. Use when the user asks to review code, review
  a pull request, review staged/unstaged changes, review a commit, or
  compare branches for code quality issues. Produces line-level review
  comments and can automatically apply fixes when requested. With appropriate
  review rules, can detect various types of issues including bugs, security
  vulnerabilities, performance problems, and code quality concerns.
license: Apache-2.0
compatibility: >
  Requires the `ocr` CLI installed (via `npm install -g
  @alibaba-group/open-code-review` or GitHub release binary). Requires a
  configured LLM (Anthropic or OpenAI-compatible) before first run.
metadata:
  author: alibaba
  homepage: https://github.com/alibaba/open-code-review
  version: "1.0.0"

Open Code Review

A skill for invoking open-code-review (ocr) — an open-source AI code review CLI that reads Git diffs and generates structured, line-level review comments.

Prerequisites check

Before starting a review, verify the environment:

# 1. Check the CLI is installed
which ocr || echo "NOT INSTALLED"

# 2. Verify LLM connectivity
ocr llm test

If ocr is not installed, install it first:

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

If ocr llm test fails, the user must configure an LLM. Guide them with one of these options:

Option A — Environment variables (highest priority, recommended for CI):

export OCR_LLM_URL=https://api.anthropic.com/v1/messages
export OCR_LLM_TOKEN=<api-key>
export OCR_LLM_MODEL=claude-opus-4-6
export OCR_USE_ANTHROPIC=true

Option B — Persistent config:

ocr config set llm.url https://api.anthropic.com/v1/messages
ocr config set llm.auth_token <api-key>
ocr config set llm.model claude-opus-4-6
ocr config set llm.use_anthropic true

Stop here and ask the user to provide credentials — never invent or hardcode API keys.

Workflow

Step 1: Gather Business Context

Analyze the review target (commits, branch, or changes) to extract concise business context. Pass this context via --background to improve review quality.

Step 2: Run Code Review

Run the OCR command with appropriate flags. Always pass business context via --background when available:

ocr review --audience agent --background "business context here" [user-args]

Argument handling:

  • Background context (RECOMMENDED): use --background "context" or -b "context" to provide business context for better review quality
  • Default (no user arguments): reviews staged, unstaged, and untracked changes (workspace mode)
  • Specific commit: use --commit or -c to review a single commit against its parent
  • Branch comparison: use --from <ref> and --to <ref> to review diff between two refs
  • Timeout: default timeout is 10 minutes per file; adjust with --timeout <minutes>
  • Concurrency: default concurrency is 8 file workers; reduce with --concurrency <n> if rate limits are hit
  • Preview mode: use --preview or -p to preview which files will be reviewed without running the LLM
  • Installation: if ocr command is not found, install it by running npm i -g @alibaba-group/open-code-review

Common invocation patterns:

User saysCommand to run
"review my changes" / "review the working copy"ocr review --audience agent -b "context"
"review this PR" / "review feature branch"ocr review --audience agent -b "context" --from main --to <branch>
"review commit abc123"ocr review --audience agent -b "context" --commit abc123
"what would be reviewed?" (dry-run)ocr review --preview

Output mode:

  • Always use --audience agent to suppress progress UI and emit only the final summary

Step 3: Classify and Report

For each comment from the review output, classify by priority and report all issues to the user:

  • High: Obvious bugs, security issues, clear mistakes, or well-founded suggestions with precise fix proposals
  • Medium: Reasonable concerns but context-dependent, style/performance suggestions, or fixes that require manual implementation
  • Low: Likely false positives, lacking sufficient context, nitpicks, or meaningless suggestions

Report all comments grouped by priority level.

Step 4: Fix

Before applying fixes, check whether the user requested automatic fixes:

  • If the user explicitly requested "review and fix" or similar, proceed with automatic fixes
  • If the user only requested "review" without fix intent, ask for permission before applying any changes

When fixing issues and suggestions:

  • Focus on High and Medium priority items
  • Apply fixes directly to the code when safe and well-defined
  • For complex fixes requiring manual intervention, clearly describe what needs to be done
  • Always verify fixes with the user before committing

Output Format

Each comment contains:

  • path: File path
  • content: Review comment text
  • start_line / end_line: Line range (both 0 means positioning failed)
  • suggestion_code: Optional fix suggestion
  • existing_code: Optional original code snippet
  • thinking: Optional LLM reasoning process

After filtering comments by priority, present results using this template:

## Code Review Results

**Files reviewed**: N
**Issues found**: X high priority / Y medium priority

### High Priority

- **`path/to/file.java:42`** — Brief description
  > Recommendation: How to fix

### Medium Priority

- **`path/to/file.ts:88`** — Brief description
  > Recommendation: How to fix (if applicable)

If the review found no issues after filtering, simply state: "Review complete — no issues found in N files."

Priority classification:

  • High: Obvious bugs, security issues, clear mistakes, or well-founded suggestions with precise fix proposals
  • Medium: Reasonable concerns but context-dependent, style/performance suggestions, or fixes that require manual implementation
  • Low: Discarded silently (likely false positives, lacking context, nitpicks, or meaningless suggestions)

Handling mispositioned comments:

When start_line and end_line are both 0, the comment failed to locate the exact position in the file. In such cases:

  1. Read the comment content to understand the issue
  2. Examine the target file mentioned in the comment
  3. Identify the relevant code section based on the comment's context
  4. Apply the fix or suggestion to the correct location

Custom Review Rules

If the user wants project-specific rules, OCR resolves them in this priority order:

  1. --rule <path> flag (highest)
  2. <repo>/.opencodereview/rule.json
  3. ~/.opencodereview/rule.json
  4. Built-in system defaults (lowest)

By default, the first matching user rule replaces the built-in system rule. Set merge_system_rule: true on a rule entry when the matched system rule and user rule should both be included.

Rule file format:

{
  "rules": [
    {
      "path": "**/*.java",
      "rule": "All new methods must validate required parameters for null",
      "merge_system_rule": true
    },
    {
      "path": "**/*mapper*.xml",
      "rule": "Check SQL for injection risks and missing closing tags"
    }
  ]
}

To preview which rule applies to a file before reviewing:

ocr rules check src/main/java/com/example/Foo.java

Gotchas

  • LLM must be configured first — ocr review will fail loudly if no LLM is reachable. Always run ocr llm test before the first review.
  • Working directory matters — ocr review operates on the Git repo at the current directory. Use --repo /path/to/repo to run from elsewhere.
  • Untracked files are reviewed in workspace mode — running bare ocr review includes staged, unstaged, and untracked changes. Stage selectively if you want narrower scope.
  • Large diffs may hit token limits — files with very large diffs may be truncated. The default MAX_TOKENS is 58888 per request.
  • Plan phase triggers at 50 lines — diffs exceeding 50 changed lines run an extra risk-analysis phase before main review. This adds latency but improves quality.
  • Don't pass --audience human — it streams progress UI that pollutes output. Always use --audience agent.
  • Comment language follows config — set language config to English or Chinese (default: Chinese) to control review comment language.

Validation

After the review completes, verify success by checking:

  1. The command exited with code 0
  2. Comments were generated (or "No comments generated" message appears)
  3. Warnings (if any) are displayed in stderr

If errors occurred, check the stderr warnings for details about which files failed and why.

References

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