Getting started with Playwright Check Suites just got a lot easier.

We added a Copy prompt button to the Playwright Check Suites onboarding. Yes, the whole config setup is now a copy-paste.

Curious about these checks but weren’t sure how to get started? You can use Playwright Check Suites to test against multiple browsers, reuse storage, and bring your own dependencies to your monitoring. Truly reusing Playwright tests as monitoring checks to ensure your customers get the best experience possible.

Here's how it works:

1. Click Copy prompt in the Checkly UI

2. Paste it into your AI coding tool in the context of your repo. Claude Code, Cursor, Copilot… (your call)

3. The prompt reads your repo's Playwright setup (projects, test files, base URLs, dependencies), and generates check suite definitions grouped by project and tags

4. It iterates and tests until the config looks right, then deploys once you’re ready

You get a working checkly.config.ts without writing one from scratch. No digging through docs. No config guessing. Repo to running checks, directly.

Also available for Test Sessions and Playwright Reporter setup. Same flow, same copy-paste, and a different prompt for each.

This is just the beginning; we're already planning what's next. Got feedback? We want it. Reach out in our Slack community or tell us here directly.

The “Yes, we rewrote it and yes, it’s better.” Release

If you’ve been here before v1, this is the “we touched basically everything” update, condensed so you don’t have to diff eight releases.

If you’re new: perfect timing.

This release turns the Checkly reporter for Playwright into the thing it was always meant to be: production-ready, CI-flexible, security-conscious, and actually helpful when things go sideways.

It runs alongside your Playwright tests, captures execution data (logs, traces, network activity, console output, performance metrics), and uploads it to Checkly so your team can debug runs without digging through CI artifacts or downloading trace files.

You can get the Playwright Reporter here.

The Checkly Playwright Reporter doesn’t just collect Playwright test results. It also surfaces debugging data that usually lives buried inside trace files or CI artifacts:

• Scrubs secrets automatically

• Uploads traces, logs, videos, screenshots and snapshots

• Shows console and network data extracted from the playwright trace

• Visualizes CPU and memory usage

• Root cause analysis by Rocky AI

• Shows steps and code snippets for every test step

• Makes CI runs shareable without sending someone .zip files and log copy-pastes

If you want to see every config option available, check out the demo repository.

🔐 Your secrets are safe now

The reporter now automatically scrubs sensitive values from reports and trace files.

You can fully control it, specifying custom variables to be scrubbed:

createChecklyReporter({ scrubbing: { envVars: ['API_KEY', 'DB_PASSWORD'], autoDetect: true, replacement: '[REDACTED]' } }) 

📊 Summary table at the end of every run

While tests are running, you get colour-coded [project] indicators in your terminal so you can quickly see what’s passing and what’s… not.

At the end of the run, you get a per-project breakdown, right in your terminal, plus deep links to traces and full results in Checkly.

⚙️ Suite Performance Graph

Now you can see CPU and memory usage over time as your Playwright test suite runs, broken down by project. Each Playwright project is highlighted on the timeline, so you can spot which one causes CPU or memory spikes. Use the Suite Performance Graph to:

• Find memory leaks

• Identify resource-heavy projects,

• Adjust your playwright configuration

• Right-size your CI machines.

🌿 Automatic git detection

Branch, commit SHA, and author are auto-detected in CI and locally. No configuration. No manual wiring. It just works.

🧠 Test step code snippets in Checkly

Step reports now include

• Root cause analysis powered by Rocky AI

• The exact source line

• Surrounding context

• Direct Trace, Snapshots, Screenshots and Video assets.

🌐 Richer network + browser console data

Network requests now include:

• Domain

• Resource type

• Headers

• Timing

• Transfer + resource bytes

🧾 Test command + run logs

The test command is now automatically detected and shown in the Checkly UI. Job logs from the run are also uploaded for you to review and share with your team.

TL;DR

If you’re running Playwright tests locally and in CI, the Checkly Playwright Reporter lets you see the same debugging insights you get from Checkly monitoring, without digging through trace files or CI logs.

Getting started takes about a minute:

1. Install the reporter

   npm install --save-dev @checkly/playwright-reporter

2. Export your Checkly Account ID and API key

   export CHECKLY_API_KEY=your_api_key 
   export CHECKLY_ACCOUNT_ID=your_account_id

3. Add the reporter to your playwright.config.ts:

   import { defineConfig } from '@playwright/test'; 
   import { createChecklyReporter } from '@checkly/playwright-reporter';
   
   export default defineConfig({ 
   reporter: [ ['list'], createChecklyReporter(), ], 
   });`

4. Run your tests npx playwright test

Now open the run in Checkly and explore traces, logs, network requests, console output and performance data.

For updates, check out the Checkly Reporter Changelog and Checkly Reporter Docs

Happy Playwright testing (and happier debugging!)

Questions or feedback? Join our Slack community.

Rocky AI — Checkly’s AI agent — is now GA. Rocky AI’s Root Cause Analysis automatically analyzes, categorizes, and summarizes failures from your monitors, synthetic checks and testing results.

Root Cause Analysis

Rocky AI’s automated Root Cause Analysis (RCA) can now analyze any failing Playwright, API, Multistep, TCP, DNS and ICMP check/monitor.

Key features are:

• Rocky AI RCA breaks down the failure into logical chunks, starting from the user perspective, via steps in your test to the underlying root cause.

• Rocky investigates all available artefacts like large Playwright trace files, binary PCAP files, trace routes and ICMP logs.

• For TypeScript-based checks, Rocky will suggest code fixes where applicable.

• Rocky AI RCA works across monitoring and testing workloads.

RCA in your inbox

You can also perform an automated initial RCA the moment a new error happens and get the analysis directly in your inbox, Slack, Teams, or any other alert channel.

Don’t worry about latency: if the initial analysis takes too long (~45 secs), we will just send the regular alert message and keep processing the RCA in the background.

If you have custom webhooks set up, the fields in the analysis are now available in new template variables:

• AI_ANALYSIS_CLASSIFICATION

• AI_ANALYSIS_USER_IMPACT

• AI_ANALYSIS_ROOT_CAUSE

• AI_ANALYSIS_CODE_FIX

• AI_ANALYSIS_LINK

Head over to the Rocky AI settings page to enable it.

Bring Your Own Model

By default, Checkly uses OpenAI’s GPT-5.1 model for AI features. However, you can also bring your own model (BYOM) and provider to Checkly. This is Enterprise feature.

• Rocky AI is free to try on all Hobby plans, with 10 included RCA analyses. Need more, upgrade via our new Resolve Plan, starting at $19/month. Check our pricing page for more details.

• If you are an Enterprise customer, first enable Rocky AI in your settings.

We’re excited to announce ICMP monitors in Checkly, now available on all plans.

ICMP monitors continuously ping your hosts to measure reachability, latency, and packet loss across regions, so you can spot connectivity issues before they impact your users.

Create your first ICMP monitor

Use Cases

ICMP monitors check whether a host is reachable by sending ICMP Echo Requests (pings). Use them to:

• Track packet loss and latency to detect degradation early

• Verify that hosts which are not exposed to the public internet remain reachable

• Catch network-level outages before they affect your application

• Compare latency across regions to understand geographic performance

Get Started With ICMP Monitors

Jump into your dashboard to add a new ICMP monitor:

1. Enter the hostname or IP address you want to monitor

2. Set your ping count and packet loss thresholds

3. Optionally add assertions (e.g. on latency)

ICMP monitors are fully integrated into Checkly’s Monitoring as Code (MaC) workflows and available in the CLI from version v7.1.0.

import { Frequency, IcmpAssertionBuilder, IcmpMonitor } from "checkly/constructs"

new IcmpMonitor('cloudflare-dns-icmp', {
  name: 'Cloudflare DNS ICMP Monitor',
  activated: true,
  frequency: Frequency.EVERY_1M,
  maxPacketLossThreshold: 20,
  degradedPacketLossThreshold: 10,
  request: {
    hostname: '1.1.1.1',
    pingCount: 20,
    assertions: [
      IcmpAssertionBuilder.latency('avg').lessThan(100),
      IcmpAssertionBuilder.latency('max').lessThan(200),
    ]
  }
})

Using Infrastructure as Code?

• Terraform (v1.18.0): checkly_icmp_monitor

• Pulumi (v2.8.0): checkly.IcmpMonitor

Learn more in our developer docs on ICMP monitoring.

See Uptime Monitoring in Action

ICMP monitors join Checkly’s uptime monitoring suite alongside URL, TCP, DNS, and Heartbeat, giving you visibility across every layer of your stack.

If you’re curious how teams use these monitors together in practice, join our upcoming webinar on uptime monitoring. We’ll walk through real-world examples of spotting reachability issues, catching DNS changes, validating TCP connections, and debugging outages.

Register here

Here’s a roundup of the new features and improvements we shipped over the past month.

Smarter Alert Notifications with Rocky AI

Rocky AI — Checkly’s AI agent — automatically analyzes, categorizes, and summarizes failures from your monitors and synthetic checks.

Now, those insights don’t just live inside the app. You can receive a Root Cause Analysis directly in your inbox, Slack, Teams, or any other alert channel the moment a check fails.

This feature is live now for all plans, head over to the Rocky AI settings page and enable it.

👉🏼 Curious to learn more? Read the full update on Rocky AI Root Cause Automation.

Dashboard Revamp: Compare Checks more easily

Dashboard charts now display data based on the selected time range, so all checks on a dashboard share the same timeline. This makes it easier to compare results and understand what happened across your services.

We’ve also added group names next to check names (where applicable), making it easier to tell similarly named checks apart.

Save Views on the Home Dashboard

You can now save dashboard views based on your selected filters and search.

Saved views are personal to your account, and you can easily share them with teammates by copying the URL.

Playwright Check Suites Improvements

We’ve made a couple of improvements to Playwright Check Suites to give you more flexibility when structuring and bundling your test code:

• Workspace support: If your project uses pnpm or npm workspaces, Checkly now detects your workspace structure, uploads the relevant files, and installs the correct dependencies automatically. Learn more about workspaces in our developer documentation.

• Support for exports: If your package.json uses exports to define entry points, Checkly now resolves them correctly during bundling.

• TSX and JSX support: Playwright checks now support .tsx and .jsx files, so you can use React-style syntax directly in your Playwright Check Suites.

• Improved Test Detail pages: You now get richer insights into each run, including test steps, source code, and network performance insights.

  

  These improvements are powered by our Checkly Playwright Reporter, which now runs for Playwright Check Suites executed in Checkly’s cloud.

• npx checkly pw-test is now GA: The pw-test command is out of beta and runs your Playwright tests against Checkly’s global cloud using your existing playwright.config.

  • Added --installCommand to customize how dependencies are installed: npx checkly pw-test --installCommand='npm install --no-scripts' -- --grep@critical.

  • Added --include to explicitly include additional files required to run your tests that are outside the directory defined in your playwright.config.

• Full dependency installation: Playwright Checks now install all dependencies defined in your package.json. Previously, only devDependencies were installed.

• Smarter dependency caching: The dependency cache now accounts for changes in the installCommand and working directory when generating the cache hash, refreshing automatically when needed.

Developer Experience

• Prometheus metrics for DNS & Heartbeat monitors: Checkly’s Prometheus integration now includes metrics for DNS and Heartbeat monitors, so you can track their performance directly in your Prometheus setup.

• View and manage Status Page subscribers: You can now access your Status Page subscribers in two ways:

  • In the web app: via the Subscribers tab on your Status Page

  • Via the API: using the /status-pages/{statusPageId}/subscriptions endpoint

• Heartbeat monitors now support incident automation: You can now link Heartbeat monitors to a Status Page, so incidents are automatically created whenever a check fails.

• Expanded phone call alerting to 16 new countries: Voice call alerts now support Italy, Mexico, Argentina, Austria, Czech Republic, Denmark, Hungary, Poland, Portugal, Spain, Uruguay, Paraguay, Chile, Iceland, Liechtenstein, and Luxembourg.

Happy monitoring!

Questions or feedback? Join our Slack community.

Playwright Check Suites now work with monorepos. If your project uses pnpm workspaces or npm workspaces, Checkly detects your workspace structure, uploads the relevant files, and installs the correct dependencies automatically.

No extra configuration needed. Point Checkly at your suite, and it resolves the dependency tree from your workspace root.

What this means for your workflow:

• Shared utility packages across your workspace are detected and included

• Workspace-internal dependencies (workspace:*) are resolved correctly

• Only the files and workspaces packages your check suite needs are uploaded, not your entire repo

New to workspaces? The pnpm workspaces docs cover the concept well, and it applies to npm workspaces too.

Get started with the latest Checkly CLI

Starting with the Checkly CLI v7.1, you can use npm and pnpm workspaces in your Playwright Check Suites to pull exactly the dependencies that you need.

Update to the latest version with

npm install checkly@latest 

If you’re new to Checkly, you may prefer to start with a guided setup:

npm create checkly@latest 

Happy monitoring!

Questions or feedback? Join our Slack community.

You can now get an AI Root Cause Analysis in your inbox, Slack or any other alert channel the moment a monitor or synthetic check fails.

To enable this new feature, just head over to the Rocky AI settings page and flip two toggles. Now, on each new failure, Rocky AI will automatically kick off a Root Cause Analysis (RCA) and add the results to your email, Slack message, Teams message etc.

Don’t worry about latency: if the initial analysis takes too long (~45 secs), we will just send the regular alert message and keep processing the RCA in the background.

If you have custom webhooks set up, the fields in the analysis are now available in new template variables:

• AI_ANALYSIS_CLASSIFICATION

• AI_ANALYSIS_USER_IMPACT

• AI_ANALYSIS_ROOT_CAUSE

• AI_ANALYSIS_CODE_FIX

• AI_ANALYSIS_LINK

This feature is live now for all plans. Head over to our docs to learn more about Rocky AI Root Cause Analysis.

We just taught Rocky AI — the Checkly AI agent — a new skill: Rocky can now root cause analyze Playwright locator.click: Test timeout exceeded. Waiting for getByRole() errors with much more context and insights. Here’s a redacted example of an error that went from “timeout exceed” to a distinct correlation between a specific backend auth call timing out and the eventual UI failing.

This category of errors are a catch all for many underlying issues and were earlier classified too often as Playwright script errors, e.g. errors in either your frontend code or your test code.

Going forward, Rocky AI will cross reference failing XHR / fetch calls to give you a clearer insight if your DB or network was failing or if you made a typo in a CSS selector.

Learn more about Rocky AI and root cause analysis.