open-code-review README

The project's README from github.com/alibaba/open-code-review. As one markdown file: README.md.

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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).

Hugging Face 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:

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:

Agent — Dynamic Decision-Making

The agent's strengths are concentrated where they matter most — dynamic decisions and dynamic context retrieval:

How to Use

Prerequisites

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 — reviews feature-branch's changes since it diverged from main (merge-base mode)
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
ocr scan --resume <session-id>   # resume an interrupted full-file scan

# Save results to a file (recommended for AI host agents)
ocr review --format json --output result.json

# 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:

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

open-code-review (alibaba/open-code-review)

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47.5K ·GitHub
Open-code-review
What is this?An AI-powered code review CLI born inside Alibaba: it reads Git diffs, sends changed files to a configurable LLM agent, and returns structured review comments with line-level precision.
Is it a standalone CLI or something that lives inside my editor?A standalone CLI — after install the ocr command works globally. There are also plugins for coding agents (Claude Code, Codex, Cursor, Kimi Code, OpenCode) and an MCP server for extending the review agent.
What does getting it running involve?Install it, then run the interactive setup: it walks you through provider selection, API key entry, and model configuration, and automatically tests connectivity before your first review.
What needs to be on my machine before it works?Git >= 2.41 is required — ocr leans on Git for diff generation, code search, and repository operations — and you must configure an LLM endpoint before reviewing code.
Will it catch every defect in my changes?No — recall is deliberately lower than general-purpose agents in exchange for precision: fewer false alarms to triage, higher F1, and roughly 1/9 of the tokens per review.
Does it run on all major operating systems?Yes — the docs cover installation across all platforms and package managers, with GitHub Release binaries also offered, so Linux, macOS, and Windows are all covered.
What happens to my code during a review?Changed files are sent to the LLM endpoint you configure — ocr reads the Git diff and the agent inspects those files through that model, so your provider choice decides where the code goes.
Why mix deterministic pipelines with an LLM agent?Purely language-driven reviews tend to cut corners on large changesets, drift off the actual code location, and fluctuate with prompt tweaks. Here engineering logic locks down file selection, bundling, and rule matching, while the agent handles dynamic decisions and context retrieval.
Can it review whole files, not just diffs?Yes — ocr scan reviews entire files, aimed at auditing unfamiliar codebases or directories where there is no meaningful diff to look at.
What is Delegation Mode?An execution mode where your coding agent runs the review with its own LLM, so no OCR API key is needed; by default OCR runs reviews with the model you configured for it.
Can reviews run automatically in CI?Yes — the docs cover GitHub Actions, GitLab CI, GitFlic CI, and Gerrit integration, so reviews can run on every pull request.
What are similar projects?Benzi, pstack-claude, openqodex, semgrep, github-mcp-server, claude-code-action, pr-agent, codex-guard
Which topics is it in?gitlab, pull-request, ci-cd, code-quality, code-review, static-analysis, harness, github-actions, cursor, anthropic, mcp-server, coding-agent
Creator
alibaba
Size
54.6 MB
Stars
47.5K
Updated
CommitOct 16Releasev1.13.1Oct 17
Age
5mo
Commits · 90 Days