{"id":477,"date":"2026-06-05T00:04:00","date_gmt":"2026-06-05T00:04:00","guid":{"rendered":"https:\/\/futurenews24.com\/index.php\/2026\/06\/05\/open-code-review\/"},"modified":"2026-06-06T07:00:16","modified_gmt":"2026-06-06T07:00:16","slug":"open-code-review","status":"publish","type":"post","link":"https:\/\/futurenews24.com\/index.php\/2026\/06\/05\/open-code-review\/","title":{"rendered":"Battle-tested at Alibaba’s scale. Hybrid structure code evaluation instrument"},"content":{"rendered":"\n
\n

\"OpenCodeReview<\/p>\n

The open supply AI code evaluation agent.<\/p>\n

\"npm\"
\n\"Build
\n\"License\"<\/p>\n

English | \u7b80\u4f53\u4e2d\u6587<\/p>\n

\n

What’s Open Code Evaluate?<\/h2>\n<\/div>\n

Open Code Evaluate is an AI-powered code evaluation CLI instrument. It originated as Alibaba Group’s inside official AI code evaluation assistant \u2014 over the previous two years, it has served tens of 1000’s of builders and recognized tens of millions of code defects. After thorough validation at large scale, we incubated it into an open supply venture for the group. Merely configure a mannequin endpoint to get began.<\/p>\n

It reads Git diffs, sends modified information to a configurable LLM through an agent with tool-use capabilities, and generates structured evaluation feedback with line-level precision. The agent can learn full file contents, search the codebase, examine different modified information for context, and produce deep critiques \u2014 not simply surface-level diff suggestions.<\/p>\n

\"Highlights\"<\/p>\n

\n

The Downside with Basic-Goal Brokers<\/h3>\n<\/div>\n

When you’ve used general-purpose brokers like Claude Code with Abilities for code evaluation, you’ve got seemingly encountered these ache factors:<\/p>\n

Incomplete protection \u2014 On bigger changesets, brokers are inclined to “reduce corners,” selectively reviewing just some information and lacking others.
\nPlace drift \u2014 Reported points steadily do not match the precise code location, with line numbers or file references drifting off track.
\nUnstable high quality \u2014 Pure-language-driven Abilities are arduous to debug, and evaluation high quality fluctuates considerably with minor immediate variations.<\/p>\n

The basis trigger: a purely language-driven structure lacks arduous constraints on the evaluation course of.<\/p>\n

\n

Core Design: Deterministic Engineering \u00d7 Agent Hybrid<\/h3>\n<\/div>\n

Open Code Evaluate’s core philosophy is to mix deterministic engineering with an agent, every dealing with what it does finest.<\/p>\n

Deterministic Engineering \u2014 Arduous Constraints<\/p>\n

For evaluation steps that should not go fallacious, engineering logic \u2014 not the language mannequin \u2014 ensures correctness:<\/p>\n

Exact file choice \u2014 Determines precisely which information want evaluation and which ought to be filtered, guaranteeing no essential change is missed.
\nGood file bundling \u2014 Teams associated information right into a single evaluation unit (e.g., message_en.properties and message_zh.properties are bundled collectively). Every bundle runs as a sub-agent with remoted context \u2014 a divide-and-conquer technique that stays steady on very massive changesets and naturally helps concurrent evaluation.
\nNice-grained rule matching \u2014 Matches evaluation guidelines to every file’s traits, maintaining the mannequin’s consideration sharply targeted and eliminating info noise on the supply. In comparison with purely language-driven rule steering, template-engine-based rule matching is extra steady and predictable.
\nExterior positioning and reflection modules \u2014 Unbiased comment-positioning and comment-reflection modules systematically enhance each the placement accuracy and content material accuracy of AI suggestions.<\/p>\n

Agent \u2014 Dynamic Resolution-Making<\/p>\n

The agent’s strengths are concentrated the place they matter most \u2014 dynamic choices and dynamic context retrieval:<\/p>\n

Situation-tuned prompts \u2014 Immediate templates deeply optimized for code evaluation, enhancing effectiveness whereas lowering token consumption.
\nSituation-tuned toolset \u2014 Distilled from deep evaluation of tool-call traces in large-scale manufacturing information \u2014 together with name frequency distributions, per-tool repetition charges, and the influence of recent instruments on the general name chain \u2014 leading to a purpose-built toolset that’s extra steady and predictable for code evaluation than a generic agent toolkit.<\/p>\n

By way of NPM (Beneficial)<\/p>\n

npm set up -g @alibaba-group\/open-code-review<\/div>\n

After set up, the ocr command is accessible globally.<\/p>\n

From GitHub Launch<\/p>\n

Obtain the newest binary from GitHub Releases:<\/p>\n

#<\/span> macOS (Apple Silicon)<\/span>
\ncurl -Lo ocr https:\/\/github.com\/alibaba\/open-code-review\/releases\/newest\/obtain\/opencodereview-darwin-arm64
\nchmod +x ocr &&<\/span> sudo mv ocr \/usr\/native\/bin\/ocr<\/div>\n

#<\/span> macOS (Intel)<\/span>
\ncurl -Lo ocr https:\/\/github.com\/alibaba\/open-code-review\/releases\/newest\/obtain\/opencodereview-darwin-amd64
\nchmod +x ocr &&<\/span> sudo mv ocr \/usr\/native\/bin\/ocr<\/p>\n

#<\/span> Linux (x86_64)<\/span>
\ncurl -Lo ocr https:\/\/github.com\/alibaba\/open-code-review\/releases\/newest\/obtain\/opencodereview-linux-amd64
\nchmod +x ocr &&<\/span> sudo mv ocr \/usr\/native\/bin\/ocr<\/p>\n

#<\/span> Linux (ARM64)<\/span>
\ncurl -Lo ocr https:\/\/github.com\/alibaba\/open-code-review\/releases\/newest\/obtain\/opencodereview-linux-arm64
\nchmod +x ocr &&<\/span> sudo mv ocr \/usr\/native\/bin\/ocr<\/p>\n

#<\/span> Home windows (x86_64) \u2014 transfer ocr.exe to a listing in your PATH<\/span>
\ncurl -Lo ocr.exe https:\/\/github.com\/alibaba\/open-code-review\/releases\/newest\/obtain\/opencodereview-windows-amd64.exe<\/p>\n

#<\/span> Home windows (ARM64) \u2014 transfer ocr.exe to a listing in your PATH<\/span>
\ncurl -Lo ocr.exe https:\/\/github.com\/alibaba\/open-code-review\/releases\/newest\/obtain\/opencodereview-windows-arm64.exe<\/p>\n<\/div>\n

From Supply<\/p>\n

git clone https:\/\/github.com\/alibaba\/open-code-review.git
\ncd<\/span> open-code-review
\nmake construct
\nsudo cp dist\/opencodereview \/usr\/native\/bin\/ocr<\/div>\n

1. Configure LLM<\/p>\n

You need to configure an LLM earlier than reviewing code.<\/p>\n

#<\/span> Choice A: Interactive config<\/span>
\nocr config set<\/span> llm.url https:\/\/api.anthropic.com\/v1\/messages
\nocr config set<\/span> llm.auth_token your-api-key-here
\nocr config set<\/span> llm.mannequin claude-opus-4-6
\nocr config set<\/span> llm.use_anthropic true<\/span>#<\/span> Choice B: Surroundings variables (highest precedence)<\/span>
\nexport<\/span> OCR_LLM_URL=https:\/\/api.anthropic.com\/v1\/messages
\nexport<\/span> OCR_LLM_TOKEN=your-api-key-here
\nexport<\/span> OCR_LLM_MODEL=claude-opus-4-6
\nexport<\/span> OCR_USE_ANTHROPIC=true<\/p>\n<\/div>\n

Config is saved in ~\/.opencodereview\/config.json.<\/p>\n

It is usually suitable with Claude Code atmosphere variables (ANTHROPIC_BASE_URL, ANTHROPIC_AUTH_TOKEN, ANTHROPIC_MODEL) and parses ~\/.zshrc \/ ~\/.bashrc for these exports.<\/p>\n

2. Check Connectivity<\/p>\n

3. Evaluate<\/p>\n

<\/p>\n

cd<\/span> your-project<\/p>\n

#<\/span> Workspace mode \u2014 evaluation all staged, unstaged, and untracked modifications<\/span>
\nocr evaluation<\/p>\n

#<\/span> Department vary \u2014 evaluate two refs<\/span>
\nocr evaluation –from principal –to feature-branch<\/p>\n

#<\/span> Single commit<\/span>
\nocr evaluation –commit abc123<\/p>\n<\/div>\n

\n

Combine with Coding Brokers<\/h3>\n<\/div>\n

OCR might be seamlessly built-in into AI coding brokers as a slash command, enabling code evaluation immediately inside your agent workflow.<\/p>\n

\n

Choice 1: Set up as a Ability<\/h4>\n<\/div>\n

Use npx to put in the OCR ability into your venture:<\/p>\n

npx expertise add alibaba\/open-code-review –skill open-code-review<\/div>\n

This installs the open-code-review ability from the talents registry, which teaches your coding agent how you can invoke ocr for code evaluation, classify points by precedence, and optionally apply fixes.<\/p>\n

\n

Choice 2: Set up as a Claude Code Plugin<\/h4>\n<\/div>\n

For Claude Code, set up the command plugin by means of the next command in Claude Code:<\/p>\n

\/plugin market add alibaba\/open-code-review
\n\/plugin set up open-code-review@open-code-review<\/div>\n

This registers the \/open-code-review:evaluation slash command, which runs OCR and robotically filters and fixes points.<\/p>\n

\n

Choice 3: Copy the Command File Instantly<\/h4>\n<\/div>\n

For a fast setup with none bundle supervisor, merely copy the command file to make use of the \/open-code-review slash command in Claude Code.<\/p>\n

Mission-level (shared with crew through git):<\/p>\n

mkdir -p .claude\/instructions
\ncurl -o .claude\/instructions\/open-code-review.md
\nhttps:\/\/uncooked.githubusercontent.com\/alibaba\/open-code-review\/principal\/plugins\/open-code-review\/instructions\/evaluation.md<\/div>\n

Consumer-level (private international use throughout all initiatives):<\/p>\n

mkdir -p ~<\/span>\/.claude\/instructions
\ncurl -o ~<\/span>\/.claude\/instructions\/open-code-review.md
\nhttps:\/\/uncooked.githubusercontent.com\/alibaba\/open-code-review\/principal\/plugins\/open-code-review\/instructions\/evaluation.md<\/div>\n
\n

Prerequisite: All integration strategies require the ocr CLI to be put in and an LLM configured. See Set up and Configure LLM above.<\/p>\n<\/blockquote>\n

OCR might be built-in into CI\/CD pipelines to automate code evaluation on Merge Requests \/ Pull Requests.<\/p>\n

The core command for CI integration:<\/p>\n

ocr evaluation
\n–from “<\/span>origin\/principal“<\/span><\/span>
\n–to “<\/span>origin\/feature-branch“<\/span><\/span>
\n–format json<\/div>\n

The –format json flag outputs machine-readable outcomes appropriate for parsing in CI scripts.<\/p>\n

See the examples\/ listing for integration examples:<\/p>\n

Command
\nAlias
\nDescription<\/p>\n

ocr evaluation
\nocr r
\nBegin a code evaluation<\/p>\n

ocr guidelines examine
\n\u2014
\nPreview which evaluation rule applies to a file path<\/p>\n

ocr config set
\n\u2014
\nSet configuration values<\/p>\n

ocr llm check
\n\u2014
\nCheck LLM connectivity<\/p>\n

ocr viewer
\nocr v
\nLaunch WebUI session viewer on localhost:5483<\/p>\n

ocr model
\n\u2014
\nPresent model information<\/p>\n

Flag
\nShorthand
\nDefault
\nDescription<\/p>\n

–repo
\n\u2014
\npresent dir
\nGit repository root<\/p>\n

–from
\n\u2014
\n\u2014
\nSupply ref (e.g., principal)<\/p>\n

–to
\n\u2014
\n\u2014
\nGoal ref (e.g., feature-branch)<\/p>\n

–commit
\n-c
\n\u2014
\nSingle decide to evaluation<\/p>\n

–preview
\n-p
\nfalse
\nPreview which information shall be reviewed with out working the LLM<\/p>\n

–format
\n-f
\ntextual content
\nOutput format: textual content or json<\/p>\n

–concurrency
\n\u2014
\n8
\nMax concurrent file critiques<\/p>\n

–timeout
\n\u2014
\n10
\nConcurrent activity timeout in minutes<\/p>\n

–audience
\n\u2014
\nhuman
\nhuman (present progress) or agent (abstract solely)<\/p>\n

–rule
\n\u2014
\n\u2014
\nPath to customized JSON evaluation guidelines<\/p>\n

–max-tools
\n\u2014
\nbuilt-in
\nMax instrument name rounds per file; solely takes impact when larger than template default<\/p>\n

–tools
\n\u2014
\n\u2014
\nPath to customized JSON instruments config<\/p>\n

#<\/span> Preview which information shall be reviewed (no LLM calls)<\/span>
\nocr evaluation –preview
\nocr evaluation -c abc123 -p#<\/span> Evaluate workspace modifications with default settings<\/span>
\nocr evaluation<\/p>\n

#<\/span> Evaluate department diff with increased concurrency<\/span>
\nocr evaluation –from principal –to my-feature –concurrency 4<\/p>\n

#<\/span> Evaluate a selected commit with verbose JSON output<\/span>
\nocr evaluation –commit abc123 –format json –audience agent<\/p>\n

#<\/span> Use customized evaluation guidelines<\/span>
\nocr evaluation –rule \/path\/to\/my-rules.json<\/p>\n

#<\/span> Preview which rule applies to a file<\/span>
\nocr guidelines examine src\/principal\/java\/com\/instance\/Foo.java
\nocr guidelines examine –rule customized.json src\/principal\/sources\/mapper\/UserMapper.xml<\/p>\n

#<\/span> View evaluation session historical past in browser<\/span>
\nocr viewer
\nocr viewer –addr :3000<\/p>\n<\/div>\n

The viewer serves session JSONL contents (LLM request messages and responses) over HTTP. It enforces a Host-header allowlist on each request: loopback names (localhost, 127.0.0.0\/8, ::1) and the concrete bind host are all the time allowed. Wildcard binds (–addr :3000, –addr 0.0.0.0:3000) and different non-loopback Hostnames should be added through the OCR_VIEWER_ALLOWED_HOSTS atmosphere variable (comma-separated):<\/p>\n

OCR_VIEWER_ALLOWED_HOSTS=evaluation.inside,ocr.lan ocr viewer –addr :3000<\/div>\n

This blocks DNS-rebinding assaults towards the native viewer.<\/p>\n

OCR resolves evaluation guidelines utilizing a four-layer precedence chain. Every layer makes use of first-match-wins: if a file path matches a sample, that rule is used; in any other case it falls by means of to the following layer.<\/p>\n

Precedence
\nSupply
\nPath
\nDescription<\/p>\n

1 (highest)
\n–rule flag
\nConsumer-specified path
\nCLI specific override<\/p>\n

2
\nMission config
\n\/.opencodereview\/rule.json
\nPer-project guidelines, might be dedicated to git<\/p>\n

3
\nInternational config
\n~\/.opencodereview\/rule.json
\nConsumer-wide private preferences<\/p>\n

4 (lowest)
\nSystem default
\nEmbedded system_rules.json
\nConstructed-in guidelines protecting widespread languages and file sorts<\/p>\n

Layers 1\u20133 share the identical JSON format:<\/p>\n

{
\n“guidelines”<\/span>: [
\n{
\n“path”<\/span>: “<\/span>force-api\/**\/*.java“<\/span><\/span>,
\n“rule”<\/span>: “<\/span>All new methods must validate required parameters for null values“<\/span><\/span>
\n},
\n{
\n“path”<\/span>: “<\/span>**\/*mapper*.xml“<\/span><\/span>,
\n“rule”<\/span>: “<\/span>Check SQL for injection risks, parameter errors, and missing closing tags“<\/span><\/span>
\n}
\n]
\n}<\/div>\n

path helps ** recursive matching and {java,kt} brace enlargement.
\nInside every layer, guidelines are evaluated in declaration order \u2014 the primary match wins.
\nIf a rule file doesn’t exist, it’s silently skipped.<\/p>\n

Config file: ~\/.opencodereview\/config.json<\/p>\n

Key
\nSort
\nInstance<\/p>\n

llm.url
\nstring
\nhttps:\/\/api.openai.com\/v1\/chat\/completions<\/p>\n

llm.auth_token
\nstring
\nsk-xxxxxxx<\/p>\n

llm.mannequin
\nstring
\nclaude-opus-4-6<\/p>\n

llm.use_anthropic
\nboolean
\ntrue | false<\/p>\n

language
\nstring
\nEnglish | Chinese language (default: Chinese language)<\/p>\n

telemetry.enabled
\nboolean
\ntrue | false<\/p>\n

telemetry.exporter
\nstring
\nconsole | otlp<\/p>\n

telemetry.otlp_endpoint
\nstring
\nOTLP collector tackle<\/p>\n

telemetry.content_logging
\nboolean
\nEmbody prompts in telemetry<\/p>\n

Surroundings variables take priority over the config file.<\/p>\n

Variable
\nGoal<\/p>\n

OCR_LLM_URL
\nLLM API endpoint URL<\/p>\n

OCR_LLM_TOKEN
\nAPI key \/ auth token<\/p>\n

OCR_LLM_MODEL
\nMannequin title<\/p>\n

OCR_USE_ANTHROPIC
\ntrue = Anthropic, false = OpenAI<\/p>\n

OpenTelemetry integration for observability (spans, metrics). Disabled by default.<\/p>\n

ocr config set<\/span> telemetry.enabled true<\/span>
\nocr config set<\/span> telemetry.exporter otlp
\nocr config set<\/span> telemetry.otlp_endpoint localhost:4317<\/div>\n

Set telemetry.content_logging to incorporate LLM prompts and responses in exported information.<\/p>\n

See CONTRIBUTING.md for growth setup, coding tips, and how you can submit pull requests.<\/p>\n

\"Star<\/p>\n

Apache-2.0 \u2014 Copyright 2026 Alibaba<\/p>\n\n

Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The open supply AI code evaluation agent. English | \u7b80\u4f53\u4e2d\u6587 What’s Open Code Evaluate? Open Code Evaluate is an AI-powered code evaluation CLI instrument. It originated as Alibaba Group’s inside official AI code evaluation assistant \u2014 over the previous two years, it has served tens of 1000’s of builders and recognized tens of millions of […]<\/p>\n","protected":false},"author":1,"featured_media":479,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/opengraph.githubassets.com\/cedb8258c936ad8da632f4168518fe8e9c3f164eabd1645cc5bc18f30b4c1d0d\/alibaba\/open-code-review","fifu_image_alt":"","jnews-multi-image_gallery":[],"jnews_single_post":[],"jnews_primary_category":[],"jnews_override_bookmark_settings":[],"jnews_social_meta":[],"jnews_override_counter":[],"footnotes":""},"categories":[5],"tags":[457,765,767,259,769,766,775,362,774,783,770,776,97,768,782,773,452,778,258,771,772,445,777,268,781,779,16,780],"class_list":["post-477","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-developer-ai-open-source-ecosystem","tag-agent","tag-alibabaopencodereview","tag-alibabas","tag-anthropic","tag-architecture","tag-battletested","tag-builtin","tag-code","tag-comments","tag-compatible","tag-deterministic","tag-finetuned","tag-github","tag-hybrid","tag-injection","tag-linelevel","tag-llm","tag-npe","tag-openai","tag-pipelines","tag-precise","tag-review","tag-ruleset","tag-scale","tag-sql","tag-threadsafety","tag-tool","tag-xss"],"yoast_head":"\nBattle-tested at Alibaba's scale. 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