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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. Install Codex or Claude, authenticate it with its native login or API-token mechanism, and select it with --runner to get started.
It reads Git diffs, sends changed files through the selected authenticated local runner 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.
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 |
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.
- Deterministic review selection — Builds a manifest from the chosen diff or scan scope, applies filtering and rule matching before model work starts, and records coverage so missing files are visible instead of silently skipped.
- 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.
- Git >= 2.41 — Open Code Review relies on Git for diff generation, code search, and repository operations.
npm install -g @alibaba-group/open-code-reviewAfter installation, the ocr command is available globally.
For other installation methods (install script, GitHub Release binary, from source), see Installation.
1. Authenticate a local runner
OCR runs through an installed Codex or Claude CLI. Authenticate the CLI you want to use with its supported native mechanism (existing login or API token); OCR never stores runner credentials itself.
codex login # or use the runner's supported API-token auth
ocr review --runner codex
ocr scan --runner claude --path internal/agent--runner is required for ocr review and ocr scan unless you are using read-only/preflight flags such as --preview. Use --runner-model <name> only when you want to override the runner's default model for one run.
For local setup, see Configuration. CI authentication is separate: CI jobs should authenticate the selected runner in that environment instead of storing OCR-owned credentials.
2. Review
cd your-project
# Workspace mode — review all staged, unstaged, and untracked changes
ocr review --runner codex
# Branch range — compare two refs
ocr review --runner codex --from main --to feature-branch
# Single commit
ocr review --runner codex --commit abc123
# Resume an interrupted range or commit review
ocr session list
ocr review --runner codex --from main --to feature-branch --resume <session-id>
# Print the review comments recorded in a saved session
ocr session comments <session-id>
ocr session comments --severity critical,high --json <session-id>
# Full-file scan — review whole files instead of a diff (no git history needed)
ocr scan --runner claude # scan the entire repository
ocr scan --runner claude --path internal/agent # scan a directory or specific files
ocr scan --runner claude --resume <session-id> # 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 OCR runner setup needed
ocr delegate preview
ocr delegate rule src/main.go src/handler.goFull 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 how the review is executed
- Local runner mode — OCR runs the review through your authenticated Codex or Claude CLI
- Delegation Mode — your coding agent runs the review directly; no OCR runner configuration required
- 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
This project exists thanks to all the people who contribute. See CONTRIBUTING.md for development setup, coding guidelines, and how to submit pull requests.
Apache-2.0 — Copyright 2026 Alibaba