Reactotron vs Limelight: What Each Tool Actually Does
Both are desktop apps for debugging React Native. They capture similar data. But they do fundamentally different things with it — and understanding the difference helps you pick the right tool.
What Reactotron does well
Reactotron has been around since 2016. It's maintained by Infinite Red, it's open source, and it's one of the most trusted tools in the React Native ecosystem. It earned that trust by doing a few things really well.
State manipulation. Reactotron doesn't just show you Redux state — it lets you interact with it. You can dispatch actions from the desktop app, subscribe to specific state slices, take state snapshots, and restore them later. If you need to put your app into a specific state to reproduce a bug, Reactotron is the fastest way to do it.
Custom logging. Reactotron's display() API lets you send structured, labeled, color-coded log entries with metadata, images, and importance flags. It's a big step up from console.log when you want organized, filterable output during development.
Plugin system. Reactotron has a mature plugin architecture. There are first-party plugins for Redux, Saga, AsyncStorage, Storybook, and MST. Third-party plugins exist for other state managers and tools. If you want to extend your debugger with custom behavior, Reactotron gives you the hooks.
Zero debug mode. Unlike React Native Debugger, Reactotron works without enabling debug mode — so your app runs at normal speed while you inspect it.
Where the tools diverge
Reactotron and Limelight capture similar raw data — network requests, state changes, console output, renders. The difference is what happens after capture.
Reactotron is an inspector. It shows you a chronological timeline of events. You scroll through, click on entries, and read the details. The interpretation — figuring out which events are related and what caused what — is your job.
Limelight is a correlation engine. It automatically links events across boundaries: a network response to the state update it triggered, to the component that re-rendered as a result. It detects patterns (race conditions, N+1 queries, render loops) and structures the output for both human reading and LLM consumption.
This is the core architectural difference, and it shapes everything else about how the two tools work in practice.
Side-by-side
| Reactotron | Limelight | |
|---|---|---|
| Approach | Manual inspection — browse a timeline | Automated correlation — events linked by cause |
| Network capture | Yes | Yes |
| State tracking | Redux, MST, Zustand (via plugins) | Redux, Zustand (automatic) |
| State manipulation | Yes — dispatch actions, restore snapshots | No — read-only |
| Render profiling | Basic benchmarking | Per-component with cause breakdown |
| Cross-boundary correlation | No | Yes — network → state → render chains |
| Pattern detection | No | Race conditions, N+1, render loops, stale closures, etc... |
| AI editor integration (MCP) | No | Yes — Cursor, Claude Code, Copilot |
| Plugin system | Yes — extensible with custom plugins | No |
| AsyncStorage | Yes — view and track changes | No |
| Storybook | Yes — built-in integration | No |
| Price | Free, open source | Free tier, open source, paid plans available |
Same bug, two tools
A search screen sometimes shows results from a previous query. Users report it happens when they type quickly. Here's how each tool helps you find the cause.
In Reactotron
You reproduce the bug while Reactotron is open. In the timeline, you see two API requests to /api/search — one for "react" and one for "react native." You also see two Redux actions updating searchResults. By checking timestamps and reading the payloads, you notice the slower request ("react", 340ms) resolved after the faster one ("react native", 120ms) and overwrote the correct results.
You figured it out by manually scanning the timeline, correlating the timestamps, and reading the payloads. Reactotron showed you the data — you connected the dots.
In Limelight
You reproduce the bug. Limelight's correlation engine automatically links the two requests, the two state updates, and the re-renders. It flags the issue:
Causal Chain:
1. GET /api/search?q="react" → 200 (340ms)
2. GET /api/search?q="react native" → 200 (120ms)
3. State: searchResults ← response from #2 (correct)
4. State: searchResults ← response from #1 (OVERWRITES #3 — stale)
Pattern: Race condition — out-of-order response resolution
Root Cause: State setter does not check request orderingIf you're using an MCP-connected editor, the AI gets this structured context directly and suggests a fix (abort controller or sequence ID) without you copying anything.
Both tools got you to the answer. Reactotron required you to read the timeline and make the connections yourself. Limelight made the connections automatically and handed you (or your AI assistant) a structured diagnosis.
When to use which
Use Reactotron when you need to interact with your app's state — dispatching actions, restoring snapshots, testing edge cases by manually putting your app into specific states.
Use Limelight when you want to understand cause and effect across multiple boundaries — why a component re-rendered, what network request triggered a state change, whether there's a race condition or render loop. Especially useful when the bug is intermittent or involves timing. Also when you want to pipe runtime context into an AI coding assistant via MCP.
