Ross ROSS = Recommend OSS · open-source software intelligence for agents

alibaba/open-code-review

Fast, efficient, battle-tested at Alibaba's scale. Hybrid architecture code review tool: deterministic pipelines + LLM Agent, precise line-level comments, built-in multi-language ruleset (NPE, thread-safety, XSS, SQL injection), OpenAI & Anthropic compatible. observed · 2026-08-28

github.com/alibaba/open-code-review · homepage · Go · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

76/100

  • Activity 99
  • Release rhythm 87
  • Longevity 7

Flags: young

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 0.0
  • age_days: 108
  • days_rel: 6
  • days_push: 7
  • n_releases_24m: 113

Full methodology

Adoption not part of the score

21468 stars · 1576 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

OpenCodeReview is an AI-powered code review CLI tool that combines deterministic analysis pipelines with an LLM Agent to produce precise line-level review comments. Originally Alibaba's internal AI code review assistant, it ships with built-in multi-language rulesets covering defects like NPE, thread-safety issues, XSS, and SQL injection, and works with OpenAI- and Anthropic-compatible models.

Use cases

  • automate code review on pull requests
  • detect null pointer and thread-safety bugs before merge
  • scan diffs for XSS and SQL injection vulnerabilities
  • add AI-generated line-level review comments to a repository
  • run LLM-based code review locally from the CLI
  • enforce consistent multi-language review rulesets across teams

When to choose

  • you want battle-tested AI code review proven at large-enterprise scale
  • you need deterministic rules plus LLM reasoning in one hybrid pipeline
  • you want precise line-level comments rather than vague file-level feedback
  • you use OpenAI- or Anthropic-compatible models and a Go/Node CLI workflow

When to avoid

  • you need fully offline review with no LLM API access
  • you only want traditional static analysis without AI commentary
  • your workflow requires deep IDE integration rather than CLI/agent-based review

Facets

cli-tool · maturity active

code-review llm-inference agent-framework security cli developer-tools developer-tools version-control security artificial-intelligence large-language-models windows cli go ai-code-review line-level-comments llm-agent static-analysis github-integration multi-language battle-tested linux macos nodejs

2 sources

Member repositories

RepositoryRoleHealth v2
alibaba/open-code-reviewmain76

For agents

markdown · JSON · MCP: product_card(name="alibaba/open-code-review")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem