Error monitoring that shows you why.

Q: What is error monitoring?

Error monitoring captures the exceptions your application throws in production, groups repeats of the same error into one issue, and tracks how often it happens, so your team can fix the errors that matter first.

Q: How is error monitoring different from logging?

Logging records what your system did, line by line. Error monitoring starts from the exception: it groups repeats into issues and keeps the stack trace. Squasher does both, and errors and logs share trace IDs, so you can move from one to the other.

Q: Is error tracking the same as APM?

No. APM measures where requests spend their time. Error tracking records what failed. Squasher accepts OpenTelemetry traces next to your errors, and a span that ends with an error status is recorded as an error.

Q: Which languages and frameworks does Squasher support?

Squasher has SDKs for the browser, Next.js, Node.js, Cloudflare Workers, and Rust, and logging handlers for Python and Go. Any service that exports OpenTelemetry can send over OTLP, and Squasher also accepts error events from Sentry SDKs.

Q: Is AI triage included on every plan?

Yes, AI triage is on every plan. It runs on new production issues, reads the error type, message, service, environment, and event counts, and writes the likely cause, the impact, and a next step. To dig into logs and traces, ask the Squasher Agent. Fix pull requests are on Team and up.

Q: Does Squasher change my code on its own?

No. A fix arrives as a pull request on your GitHub repository, with a regression test when the failure can be reproduced safely. Your team decides whether to merge it. Squasher never merges on its own.

Q: How does Squasher handle sensitive data?

The SDKs have a beforeSend hook, so you can drop or edit an event before it leaves your app. At ingest, values under keys such as password, token, secret, cookie, and authorization are replaced with [REDACTED]. Session replay masks all page text, typed values, and media by default.

Squasher groups exceptions into issues, ties them to the logs and traces from the same request, and AI triage names the likely cause.

Start free

Free plan. No credit card required.

app.squasher.ai/acme/checkout-api/issues/c4e1a92f-6b1d-4e0a-9d7e-2a51f0c3b8e4

acmecheckout-apiIssuesC4E1A92F

Search⌘ K

TypeError: Cannot read properties of undefined (reading 'amount')

Review impact, context, and the next response action.

Investigate with Squasher

HighUnresolvedError

AI triage

checkout-api throws a TypeError in production when it reads amount from an undefined value: 1,339 events in 12 minutes. Likely cause: a price is read before it has loaded. Impact: high, the failing requests are checkouts. Next step: guard the read of amount and wait for the price.

Events: 1,339
First seen: 12 min ago
Last seen: 1 min ago

Fingerprint

9f2c41e8a0b7d3c6

Issue

Status: Unresolved
Priority: High
Environment: production
Service: checkout-api
Assignee: Squasher AI

Workflow

BookmarkSubscribeRun AI triageCreate fix

Example project and data

Works with the stack you already run.

Next.js
Node.js
Browser
Cloudflare
Rust
Python
Go
OpenTelemetry
Vercel
AWS

Everything an error needs to get fixed.

A stack trace says where it broke. Squasher adds the release, the user, and the request around it.

Grouped into issues
Repeats of the same error become one issue, with its event count and when it was first and last seen.
A workflow, not a feed
Resolve, ignore, or mute an issue. Set its priority, assign it, and merge duplicates.
Release and environment
Every event carries the release and environment you pass to the SDK, so you know which deploy it came from.
User and tags
Identify the signed-in user and add your own tags, so every error says who hit it and where.
Linked by trace ID
Errors, logs, and spans share trace IDs. Follow one failure through all three.
Built for agents
List, update, and triage issues from the CLI, the REST API, or your coding agent over MCP.

Follow one failure across every signal.

Errors, logs, and traces share trace IDs. Browser errors and replays share a session ID.

1. 14:06:09infoPOST /api/checkout 200 188ms
2. 14:06:11infocart.load cart=8f21 items=3
3. 14:06:12warnpricing: price pending at render
4. 14:06:12errorTypeError: reading 'amount' of undefined
5. 14:06:14infoPOST /api/checkout 500 2.31s
6. 14:06:15warnretry pricing.load attempt=2
7. 14:06:16infoGET /api/cart 200 84ms
8. 14:06:18errorTypeError: reading 'amount' of undefined
9. 14:06:09infoPOST /api/checkout 200 188ms
10. 14:06:11infocart.load cart=8f21 items=3
11. 14:06:12warnpricing: price pending at render
12. 14:06:12errorTypeError: reading 'amount' of undefined
13. 14:06:14infoPOST /api/checkout 500 2.31s
14. 14:06:15warnretry pricing.load attempt=2
15. 14:06:16infoGET /api/cart 200 84ms
16. 14:06:18errorTypeError: reading 'amount' of undefined
Logs
Open a log line to see its trace and the lines around it.
1. POST /api/checkout
2. cart.load
3. pricing.load
4. db.query prices
5. render.total
6. 0 msrender.total threw2.31 s
Traces
Spans from OpenTelemetry or the SDK. A failed span is an error.
shop.example.com/checkout
Total
Pay
Something went wrong
error at 1:031:40
Session replay
What the user saw, under the same session ID as their errors.

Previews show example data.

A first diagnosis on every new issue.

AI triage is on every plan. It runs on new production issues and writes its diagnosis on the issue page.

Reads the error type, message, service, environment, and event counts.
Writes the likely cause, the impact, and a next step.
Create fix works in your repository and opens a pull request after your tests pass, on Team and up. You merge it.
Need more? The Squasher Agent searches your logs and traces when you ask.

How AI triage works

app.squasher.ai/acme/checkout-api/issues/c4e1a92f

TypeError: Cannot read properties of undefined (reading 'amount')

Review impact, context, and the next response action.

Investigate with Squasher

HighUnresolvedError

AI triage

Events: 1,339
First seen: 12 min ago
Last seen: 1 min ago

Fingerprint

9f2c41e8a0b7d3c6

Issue

Status: Unresolved
Priority: High
Environment: production
Service: checkout-api
Assignee: Squasher AI

Workflow

BookmarkSubscribeRun AI triageCreate fix

[Squasher] Fix: TypeError — Cannot read properties of undefined (reading 'amount')

OpenOpened by the Squasher hosted agent · #482

src/payments/currency.ts+2 −1
src/payments/currency.test.ts+6

All checks have passedReview required

Example issue and pull request

Error spikes page the right people.

A query monitor watches your error volume and opens an incident when it jumps.

Alert on a fixed threshold, or on a spike over the usual baseline.
Route by severity to Slack, email, webhooks, PagerDuty, or Telegram.
Slack cards name the impact, the likely cause, and a deploy from the hour before.

Notification routing

Example alert.

Live in three steps.

Add the SDK, initialize it from your environment, and send one real event. Or paste one prompt into your coding agent and let it do all three.

Install the SDK
Next.js shown. The Node.js, browser, Cloudflare Workers, and Rust SDKs work the same way.
```
bun add @squasher-ai/nextjs
```

Initialize it

Read the API key and project ID from environment variables. Never hard-code the key.

import { init } from "@squasher-ai/nextjs"; export async function register() {  init({    apiKey: process.env.SQUASHER_API_KEY!,    projectId: process.env.SQUASHER_PROJECT_ID!,    environment: process.env.NODE_ENV,  });}

Next.js guide

Send one real event
Throw a test error and check that it reaches your project.
Quickstart

Paste into your coding agent

Install Squasher for this project. If Squasher skills or MCP are not configured in this agent environment, run `bash <(curl -fsSL https://squasher.ai/install.sh) --agents -y`. Then read https://skills.squasher.ai/squasher-onboard/SKILL.md, map every runnable app/service/worker/frontend/agent in the repo, choose the matching Squasher SDK, log drain, OTLP, or Agent SDK path, initialize with SQUASHER_API_KEY and SQUASHER_PROJECT_ID from environment variables, send one real verification event, and report the files changed plus the verification step. Do not hardcode or reveal real API keys.

bash <(curl -fsSL https://squasher.ai/install.sh) --agents -y

Error monitoring, logging, and APM.

Each tool starts from a different signal. The fastest fixes use all three.

Starts from

Error monitoring: An exception
Log management: A log line
APM: A request or span
Squasher: Any of the three, by trace ID

Answers

Error monitoring: What broke, and how often
Log management: What the system did
APM: Where a request spent its time
Squasher: What broke, what led up to it, and the likely cause

Best for

Error monitoring: Exceptions your users hit
Log management: Debugging and audit trails
APM: Latency and slow dependencies
Squasher: Teams that want all three in one place

How error monitoring, log management, and APM differ, and how Squasher covers them.
Question	Error monitoring	Log management	APM	Squasher
Starts from	An exception	A log line	A request or span	Any of the three, by trace ID
Answers	What broke, and how often	What the system did	Where a request spent its time	What broke, what led up to it, and the likely cause
Best for	Exceptions your users hit	Debugging and audit trails	Latency and slow dependencies	Teams that want all three in one place

Questions, answered.

What is error monitoring?

Error monitoring captures the exceptions your application throws in production, groups repeats of the same error into one issue, and tracks how often it happens, so your team can fix the errors that matter first.

How is error monitoring different from logging?

Logging records what your system did, line by line. Error monitoring starts from the exception: it groups repeats into issues and keeps the stack trace. Squasher does both, and errors and logs share trace IDs, so you can move from one to the other.

Is error tracking the same as APM?

No. APM measures where requests spend their time. Error tracking records what failed. Squasher accepts OpenTelemetry traces next to your errors, and a span that ends with an error status is recorded as an error.

Which languages and frameworks does Squasher support?

Squasher has SDKs for the browser, Next.js, Node.js, Cloudflare Workers, and Rust, and logging handlers for Python and Go. Any service that exports OpenTelemetry can send over OTLP, and Squasher also accepts error events from Sentry SDKs.

Is AI triage included on every plan?

Yes, AI triage is on every plan. It runs on new production issues, reads the error type, message, service, environment, and event counts, and writes the likely cause, the impact, and a next step. To dig into logs and traces, ask the Squasher Agent. Fix pull requests are on Team and up.

Does Squasher change my code on its own?

No. A fix arrives as a pull request on your GitHub repository, with a regression test when the failure can be reproduced safely. Your team decides whether to merge it. Squasher never merges on its own.

How does Squasher handle sensitive data?

The SDKs have a beforeSend hook, so you can drop or edit an event before it leaves your app. At ingest, values under keys such as password, token, secret, cookie, and authorization are replaced with [REDACTED]. Session replay masks all page text, typed values, and media by default.

On squasher.ai

In the docs

Priced by volume, never by seat.

Start free. Invite the whole team. AI triage is included on every plan.

Compare every plan

Free

For side projects, evals, and internal tools.

$0/ month

No card needed. No overage charges.

10 GB telemetry
5K replays
500 AI credits

Included

AI triage on new issues
500 AI credits / month (an investigation uses 50)
1 project

Pro

For small production apps that want one tool, not five.

$29/ month

Flat price. No per-seat fees.

100 GB telemetry
10K replays
3K AI credits

Everything in Free, plus

3,000 AI credits / month (an investigation uses 50)
5 projects
30 monitors with 30-second checks

Team

Scale

For high-volume teams with security, procurement, and support needs.

$299/ month

Flat price. No per-seat fees.

1 TB telemetry
150K replays
30K AI credits

Everything in Team, plus

30,000 AI credits / month (an investigation uses 50)
100 status pages
250 monitors with 30-second checks

Need more volume, a security review, or custom terms? Get in touch.

Go squash some bugs.

Connect one service and send one real event. The crew takes it from there.