Compare/React Doctor vs Cody Enterprise 3.0

AI tool comparison

React Doctor vs Cody Enterprise 3.0

Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.

R

Developer Tools

React Doctor

Catch every anti-pattern your AI agent baked into your React app

Ship

75%

Panel ship

Community

Paid

Entry

React Doctor is a one-command static analysis tool that scans your React codebase and outputs a health score from 0 to 100 alongside a detailed diagnostic report. Run `npx react-doctor@latest .` and it identifies anti-patterns across six dimensions: state & effects, performance, architecture, security, accessibility, and dead code. It auto-detects your framework (Next.js, Vite, React Native) and React version, adjusting rules accordingly. The tool was built by Million.co—the team behind the Million.js performance library—and is clearly aimed at the post-AI-coding era. Its killer feature might be the "agent instruction installation" mode: it teaches Claude Code, Cursor, and other coding agents the project's quality rules, so future agent-written code conforms to them before React Doctor even runs. It also integrates with GitHub Actions and can post PR comments with health score diffs, making it easy to catch regressions before merge. With 8.7K stars and one of today's fastest-growing GitHub repos, the timing is perfect. Developers are increasingly shipping agent-written React code they didn't review line by line, and React Doctor fills the gap. It's MIT-licensed, requires no config to get started, and the CI integration takes about five minutes to set up.

C

Developer Tools

Cody Enterprise 3.0

AI coding assistant with unlimited multi-repo context and SOC 2 audit logs

Ship

100%

Panel ship

Community

Free

Entry

Cody Enterprise 3.0 is Sourcegraph's AI coding assistant built for large engineering organizations, extending context retrieval across unlimited repositories simultaneously so developers get answers that understand the full codebase. It adds SOC 2-compliant audit logging for every AI interaction, satisfying the compliance requirements that block enterprise AI adoption. Bring-your-own-model support lets teams swap in their preferred LLM without losing the context layer.

Decision
React Doctor
Cody Enterprise 3.0
Panel verdict
Ship · 3 ship / 1 skip
Ship · 4 ship / 0 skip
Community
No community votes yet
No community votes yet
Pricing
Open Source (MIT)
Enterprise pricing (contact sales); Cody Free tier available for individuals
Best for
Catch every anti-pattern your AI agent baked into your React app
AI coding assistant with unlimited multi-repo context and SOC 2 audit logs
Category
Developer Tools
Developer Tools

Reviewer scorecard

Builder
80/100 · ship

The GitHub Actions integration with PR health score diffs is the feature I didn't know I needed. Installing it took three minutes and immediately flagged three useEffect anti-patterns Cursor introduced last week.

78/100 · ship

The primitive here is honest and specific: a context retrieval layer that indexes across unlimited repos and pipes relevant code into whatever LLM you bring. That's a real problem — the moment your codebase spans more than one repo, GitHub Copilot and Cursor both go partially blind. The BYOM configuration is the right DX bet; it puts complexity in config where it belongs and lets the context engine be the actual product rather than a forced model subscription. The moment of truth is asking a question that spans three repos — if that actually works without hallucinating package boundaries, this earns its enterprise price tag. What I want to see is the indexing pipeline documented: how fresh is the context, what's the staleness model, and does it handle monorepos differently than polyrepos? Those aren't marketing questions, they're the whole product.

Skeptic
45/100 · skip

Static analysis for React isn't new—ESLint with react-hooks/exhaustive-deps, Biome, and others already catch most of these patterns. The 'health score' framing may encourage false confidence if teams focus on the number rather than the individual findings.

72/100 · ship

The direct competitors are GitHub Copilot Enterprise and Cursor with codebase indexing — and neither of them has Sourcegraph's decade of code search infrastructure underneath. That history is the actual moat, not the AI wrapper on top. Where this breaks: organizations with highly fragmented access controls across repos, where the context retrieval either over-fetches (security problem) or gets permission-gated into uselessness. The SOC 2 audit logs are table stakes for any enterprise deal in 2026, so calling that a feature is a bit rich — but shipping it before competitors formalized it matters. What kills this in 12 months: GitHub ships deeper Copilot Enterprise context natively and the org that was already paying for GitHub Enterprise doesn't want a second line item. Sourcegraph survives that only if the context quality gap stays wide enough to justify the cost.

Futurist
80/100 · ship

Teaching agents the rules upfront rather than fixing their output afterward is the right architectural direction. As agent-written code becomes the norm, tools that close the feedback loop at the prompt level will be as important as compilers.

No panel take
Creator
80/100 · ship

For designer-developers who use Cursor or v0 to prototype quickly, this is a sanity check that doesn't require deep React expertise. A green health score before shipping is a meaningful confidence boost.

No panel take
Founder
No panel take
75/100 · ship

The buyer is the VP of Engineering or CISO at a company with 200+ engineers across multiple repos — this is a clear, checkbook-holding persona, and SOC 2 audit logs are specifically the procurement unlock that moves deals out of legal limbo. That's a real wedge. The BYOM configuration is smart revenue-defensibility: Sourcegraph becomes the context layer that persists regardless of which model wins the next benchmark cycle, insulating them from the commodity model price war. The risk is the expand story — once they land an enterprise, what does deeper adoption look like? If it's just more seats, they're a seat-count business, and seat-count businesses get squeezed when headcount freezes. The specific decision that makes this viable is owning the index, not the model — the index is sticky, the model is not.

PM
No panel take
71/100 · ship

The job-to-be-done is clean: get an accurate, context-aware answer about code that lives in more than one repository without switching tools or copy-pasting context manually. That's one job, no 'and.' Onboarding for enterprise is always an IT/procurement journey, not a 2-minute trial, so I won't penalize that — but the individual free tier needs to get a solo dev to a cross-repo answer in under 5 minutes or it never seeds the enterprise deals. The product opinion is strong: Sourcegraph has committed to the context layer being the product, which means they're not trying to win on model quality. That's the right call given their history. The gap is that 'unlimited repositories' as a marketing claim needs to be stress-tested publicly — if there's a practical ceiling at 50 repos or 10M LOC, that needs to be in the docs, not discovered during a pilot.

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