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EnglishHow TrackJS Helps You Debug Production JavaScript Errors
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TrackJS is a frontend-native JavaScript error monitoring tool that captures what broke in production, reconstructs the events leading up to it, and helps you filter and fix it. It fits teams shipping JavaScript (including AI-generated, contractor, or third-party code) who need runtime context rather than just a stack trace. A free trial is available and, per the site, requires no credit card.
What TrackJS actually captures
When an error fires in a user's browser, TrackJS records the error alongside the runtime context around it. The site describes this as giving you "the full story behind every error" — the goal being to show what broke, how it happened, and how often, so you can fix it before it escalates into an incident.
The tool is described as private by default, with zero page overhead, and compatible with every browser and framework.
The four mechanisms that do the debugging work
1. Telemetry Timeline — reconstruct the sequence before the error
Instead of a single stack trace, you get a chronological timeline of what happened before the error:
- Network requests
- User interactions
- Console logs
- Navigation events
This matters because most production errors aren't caused by the line that throws — they're caused by a sequence of events (a failed request, then a click, then a navigation). The timeline lets you see that sequence rather than guess at it.
2. Discoverable Error Filters — find the pattern without a query language
Filtering is designed to require no query language and no learning curve. You get:
- One-click filters
- Automatic highlighting of trends across users, browsers, and pages
- Instant search to zero in on what matters
The practical effect: when an error spikes, you can quickly answer "is this one user, one browser, or everyone?" without writing a query.
3. Unlimited Ignore Rules — keep the dashboard actionable
Third-party scripts and expected errors generate noise. TrackJS provides server-side ignore rules, unlimited on every plan. You create custom rules based on any error criteria to automatically filter out:
- Known issues
- Third-party glitches
- Low-priority warnings
The result is a dashboard focused on errors that actually impact users.
4. AI Code Debugger — turn the error into a suggested fix
The AI Debugger analyzes the error telemetry, browser data, and source code to explain what went wrong and how to fix it. According to the site, it surfaces senior-level debugging insights including framework-specific quirks and browser edge cases.
How the pieces fit together
| Stage | Mechanism | What you get |
|---|---|---|
| Error occurs | Error capture | The error plus runtime context |
| Understand it | Telemetry Timeline | Chronological view of requests, interactions, logs, navigation |
| Triage it | Discoverable Filters | One-click filtering by user, browser, page; trend highlighting |
| Reduce noise | Unlimited Ignore Rules | Server-side rules filtering known/third-party/expected errors |
| Fix it | AI Code Debugger | Explanation of cause and suggested fix |
Getting started
Installation is described as adding TrackJS to your frontend in minutes, with an 8kb footprint. From there, errors flow into the dashboard where the timeline, filters, ignore rules, and AI Debugger apply.
When TrackJS is a good fit
- You ship JavaScript to production and errors only appear for real users, not in your test suite.
- You're shipping AI-generated, contractor, or third-party code and need runtime context to debug what those sources miss.
- Your current error feed is noisy and you want server-side ignore rules without paying extra per plan.
- You want filtering that a non-specialist can use without learning a query syntax.
If you only need local stack traces during development, or you don't yet have production traffic, the runtime-context features have less to work with.