Sentry tells you what broke. Error Pipeline fixes it.
Error Pipeline captures every production crash and unhandled exception in real time — then hands it directly to the autonomous repair engine. You get a pull request, not a 3am alert.
Monitoring without fixing is just a better alarm clock.
Sentry fires an alert. Your phone rings. A developer wakes up. They spend four hours reading logs, finding the broken line, writing a fix, testing it, and deploying it. Every single time.
Error Pipeline captures the same crash — and instead of ringing your phone, it reads the broken code, writes the fix, puts it through an independent AI review, and opens a pull request. You wake up, read it, click approve.
Done in 30 seconds. Nothing shipped without your sign-off.
Crash captured in 3ms
The lightweight SDK hooks into your application runtime. The moment an exception occurs, the full stack trace, environment parameters, and execution context are captured and dispatched to the pipeline — in under 3ms, with zero performance overhead on your app.
Root cause mapped automatically
The analysis engine cross-references the stack trace against your repository structure and your Institutional Logic rules if you've defined them. It identifies the exact file, the exact line, and the exact cause — before a human has even read the alert.
Fix generated in an isolated sandbox
The error context is routed to an isolated builder environment. The engine checks out your affected branch, generates a precise patch, and runs compiler and security checks to validate the fix before it ever leaves the sandbox.
Pull request opened. Team notified.
A clean pull request lands in your GitHub repo showing exactly what changed and why. Simultaneously your team gets a Slack or email notification with the issue summary, telemetry data, and a direct link to review the PR. Nothing merges without your approval.
Runtime exceptions
Unhandled errors, uncaught promise rejections, and application crashes — captured at the edge the moment they occur.
Stack traces with full context
Not just the error message — the full execution context, environment parameters, and request metadata that caused it.
CI pipeline failures
Build breaks and compiler errors caught in your pipeline before they reach production users.
Production health telemetry
Continuous visibility into your application's error rate, resolution time, and overall health — all in your DeployOwl dashboard.
Three lines. That's the whole setup.
Install the SDK, call init() with your API key, and every unhandled exception is captured automatically. No config files, no sentry.init ceremony, no manual error boundaries.
import { init, captureError, setUser } from '@deployowl/deployowl';
// 1. Initialize once at startup — unhandled errors are captured automatically
init({ apiKey: process.env.DEPLOYOWL_KEY });
// 2. (Optional) Tag errors with the affected user
setUser({ id: 'user_9482', email: 'ada@example.com' });
// 3. (Optional) Capture a handled error with extra context
try {
await riskyOperation();
} catch (err) {
captureError(err, { step: 'checkout', orderId: 'ord_8842' });
}Works in Next.js, Node.js, Deno, and Bun. Install with npm install @deployowl/deployowl.
What happens after init()
Every unhandled exception is caught and sent to your dashboard — stack trace, request context, and runtime metadata, with zero boilerplate.
Wrap a try/catch and call captureError(err, { ... }) to log handled errors with custom context — without throwing.
Each captured error can trigger an AI-generated patch, reviewed by an independent verifier, and opened as a pull request for you to approve.
Your last 3am debugging session.
Connect Error Pipeline to your project and let it handle the next crash.