# OpenCodeReview (ocr) | Artificialus > For the complete content index, see [llms.txt](https://artificialus.com/llms.txt). Markdown versions of all pages are available by appending `.md` to any URL. - Home - / - Agents - / - OpenCodeReview (ocr) O # OpenCodeReview (ocr) Alibaba's hybrid code-review CLI: deterministic pipelines + LLM agent, precise line-level comments at 1/9 the tokens Alibaba Open source Since 2026 Visit Website Repository Docs Download Share X Facebook Reddit Telegram Bluesky Email OpenCodeReview (ocr) is Alibaba's open-source AI code-review CLI. It reads git diffs and drives an LLM agent through a hybrid architecture where deterministic pipelines handle file selection, rule matching, and comment positioning while the agent does the reasoning. Built on two years of internal use at Alibaba scale, it ships a multi-language ruleset and consumes roughly 1/9 of the tokens of general-purpose agents. ## + Pros - Precision-first review: higher Precision and F1 than general-purpose agents (Claude Code) on AACR-Bench with the same underlying model, so reviewers triage fewer false alarms - ~1/9 the token consumption of general-purpose agents — a typical review pass costs a fraction of a cent on cheap models and keeps CI latency low - Deterministic pipelines guarantee complete file coverage and stable comment positioning on large changesets, where language-driven agents drift or skip files - Fully self-hosted with BYO LLM key (Anthropic, OpenAI, DeepSeek, DashScope, Z.AI or any OpenAI-compatible endpoint) — code never leaves your infrastructure - Delegation mode offloads the review to your existing coding agent (Claude Code, Codex, Cursor, Kimi, OpenCode) — OCR handles file selection and rule resolution, no API key required ## − Cons - Recall is deliberately lower than general-purpose agents — the precision-first trade-off means some real defects can slip through; pair it with other checks for security-critical code - No official hosted/managed offering — you bring the model key and run the CLI or CI integration yourself, so there is no zero-ops option - Requires Git >= 2.41 and a working local clone; it is a diff-first tool, so it is less useful for teams that do all review in a web UI ## Pricing ### Free (Open Source) $0 Apache-2.0 licensed CLI, self-hosted. Bring your own LLM API key — Anthropic, OpenAI, DeepSeek, DashScope, Z.AI or any OpenAI-compatible endpoint. No per-seat cost. ## Introduction OpenCodeReview (ocr) is Alibaba's AI code-review CLI, open-sourced in May 2026 after two years as the company's internal official review assistant — serving tens of thousands of developers and flagging millions of code defects before it ever shipped publicly. The `ocr` command reads git diffs, drives a configurable LLM agent, and returns structured review comments pinned to exact lines. The architecture is where it departs from the pack. Instead of a purely language-driven agent, it combines deterministic pipelines with an LLM agent: engineering logic handles the steps that must not go wrong — file selection, bundling, rule matching, comment positioning — while the agent handles dynamic context retrieval and reasoning. The result behaves like a battle-tested linter with judgment: complete coverage on large changesets, stable line-level accuracy, and about 1/9 of the token consumption of general-purpose agents on the same model. ## Key Features - Line-level review comments — findings are pinned to exact `file:line` positions via external positioning and reflection modules, eliminating the position drift common with prompt-driven reviewers. - Multi-language ruleset — built-in rules cover null-pointer dereferences, thread-safety, XSS, and SQL injection across languages, with template-engine-based rule matching that keeps the model's attention focused. - `ocr scan` full-file audit — reviews entire files rather than diffs, for auditing unfamiliar codebases or directories with no meaningful git history. - Delegation mode — `ocr delegate preview` lets OCR handle file selection and rule resolution while your coding agent (Claude Code, Codex, Cursor, Kimi, OpenCode) runs the review with its own LLM. No OCR API key required. - Session resume — interrupted reviews resume with `ocr session list` and `--resume `, and replay later in the browser-based Session Viewer. - MCP server — exposes review capabilities over the Model Context Protocol so external tools can extend the review agent. ## How It Works The hybrid design splits responsibilities deliberately. Deterministic engineering imposes hard constraints on the review process: precise file selection decides exactly which files need review and which should be filtered; smart file bundling groups related files (e.g., paired `.properties` files) into single review units, each run as a sub-agent with isolated context — a divide-and-conquer strategy that stays stable on very large changesets and supports concurrent review. Fine-grained rule matching uses a template engine rather than free-form prompts, and independent positioning/reflection modules fix both the location and the content accuracy of every comment. The agent is reserved for what it does best — dynamic decisions and dynamic context retrieval. Its prompt templates and toolset are scenario-tuned for code review, distilled from analysis of tool-call traces in large-scale production data (call frequency, per-tool repetition, impact on the call chain). The result is a purpose-built agent loop that is more stable and predictable for review than a generic agent toolkit. ## Benchmark Alibaba published AACR-Bench, a real-world review benchmark built from 50 popular open-source repositories, 200 real pull requests, and 10 programming languages, cross-validated by 80+ senior engineers on 1,505 annotated ground-truth issues (dataset on Hugging Face). > Compared to general-purpose agents (Claude Code) on the same underlying model, OpenCodeReview reports significantly higher Precision and F1 while consuming ~1/9 of the tokens and completing reviews faster. The honest caveat: its Recall is lower — a deliberate trade-off favoring precision over noise. You get fewer false alarms to triage, but some real defects can slip through, so it pairs best with additional checks on security-critical paths. ## Getting Started Install with one command (requires Git >= 2.41): ``` `npm install - g @alibaba - group / open - code - review` ``` Configure a provider interactively (`ocr config provider`, `ocr config model`) — presets ship for Anthropic, OpenAI, DashScope, DeepSeek, and Z.AI, plus any custom OpenAI/Anthropic-compatible endpoint, so code stays on your infrastructure. Then review: ``` ` ocr review # review all workspace changes ocr review --from main --to feature-branch # branch range ocr review --commit abc123 # single commit ocr scan --path internal / agent # full-file audit ocr review --format json --output result . json # structured output for CI ` ``` Full reference lives at open-codereview.ai/docs. ## Integrations OpenCodeReview plugs into the surrounding ecosystem instead of locking you in: CI/CD via GitHub Actions, GitLab CI, GitFlic CI, and Gerrit; coding agent plugins for Claude Code (`/open-code-review:review`), Codex, Cursor, Kimi Code, OpenCode, and QCA Forward; a portable agent skill (`npx skills add alibaba/open-code-review --skill open-code-review`); plus OpenTelemetry telemetry for observability. ## Verdict OpenCodeReview is the credible open-source challenger to commercial review tools like CodeRabbit: the same frontier models under the hood, but precision-first output at a fraction of the tokens, with complete control over where your code goes. Engineering teams and maintainers who want low-noise, self-hosted AI review — and already pay for an LLM key — should try it. Teams that need exhaustive recall on security-critical changes should treat it as one layer of a broader review stack, not the only one. ## Further Reading - GitHub Repository — Source code, releases, issues, and the AACR-Bench evaluation - Official Website & Docs — Quickstart, CLI reference, review rules, and CI/CD guides - AACR-Bench Dataset — 200 real PRs, 10 languages, 1,505 annotated issues ## Version History 1.12.9 Sep 22, 2026 Session viewer fixes, credential command validation hardening, GitLab multiline comments 1.12.8 Sep 21, 2026 F# review support; dependency and build-output directories excluded by default 1.12.7 Sep 19, 2026 Session export to self-contained HTML file 1.0 May 18, 2026 Initial open-source release of Alibaba's internal AI code-review assistant Best forEngineering teams and open-source maintainers who want precise, low-noise AI code review with full data privacy and no per-seat SaaS cost CapabilityDeterministic × agent hybrid architecture, line-level comment precision, ~1/9 token consumption vs general-purpose agents, delegation mode for coding agents, multi-language ruleset (NPE, thread-safety, XSS, SQL injection) Runs onCLI · macOS · Windows · Linux · CI/CD Signature Snippet Copy ``` `After installing via `npm install -g @alibaba-group/open-code-review`, a developer runs `ocr review --from main --to feature-branch` inside the repo. The CLI reads the diff, selects and bundles the changed files, and sends them to the configured LLM. Minutes later it returns structured review comments pinned to exact lines — a null-pointer dereference in a Go handler, a missing SQL-injection guard on a new endpoint — with zero noise on the untouched files.` ``` ## More in this Space PA ### Prime Agent (Prime Intellect) Open source Prime Agent is an open-source (MIT) self-improving coding and research harness by Prime Intellect, built on a Recursive Language Model that runs inside a persistent IPython REPL and a Continual Harness that refines its own state via /refine. It took the same frontier weights from 30.2% to 95.5% on ARC-AGI-3 RHAE Best@1, powered daemon-backed sessions, and ships no hosted offering. View profile DZ ### Delta (by Zed) Closed source Multiplayer environment from Zed for coding with agents. Threads unify the agent conversation, the code edits, and human review in one shareable artifact, replacing the branch/PR loop. Built on DeltaDB, a CRDT-based version control layer that records every edit between commits alongside the prompts and reasoning that produced them. View profile VI ### Vix Open source Vix is a Go-native, open-source (AGPL-3.0) AI coding agent that slashes token costs by 40-50% using a stem agent architecture and Tree-sitter virtual filesystem. It rethinks the plan/execute loop — keeping LLM cache warm across Explore, Plan, and Execute phases — while shipping Programmable Workflows, Whiteboard Mode with voice AI, MCP server support, and a self-evolving agent that writes its own scheduled jobs and watchers. View profile