A failed build that explains itself
inline watches your GitHub Actions runs. A failure gets a root-cause comment on the pull request that caused it, so the person who broke the build reads why without opening anything else. Flaky tests and builds that have crept slower than their baseline collect in one list.
Three signals,
each with a badge
A failure is a run that failed: inline reads the logs, works out why, and comments the cause on the pull request. Flaky is a run that failed and then passed on a re-run with no code change. Slow run is a workflow taking noticeably longer than its own baseline, so a build that has drifted slower shows up before anyone times it.
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How it works
Real breakage
or flakiness
The overview counts open alerts by signal, which is the quickest way to see whether the team is dealing with broken code or untrustworthy tests. Success rate and failed runs over the last 14 days sit across the top beside open alerts and what the triage has cost.
Watch what
you choose
One switch per signal: AI failure triage, flaky test detection and build duration tracking, under one master switch. Decide whether new repositories are monitored by default, switch each repository on or off by hand with an optional branch filter, and choose whether alerts also arrive by email.
Included
Related
Cloud monitoring
Connect an Azure subscription with one pasted command and read-only roles. inline turns its posture, health and spend into a prioritized list of findings.
Read moreRuntime errors
An SDK sends the crashes your users hit. inline groups them into one issue per bug, prioritizes each one and names the change that shipped it.
Read morePrompt evals
inline finds the LLM prompts in your repository, writes happy path, edge, adversarial and boundary cases for each, runs them and grades the answers.
Read more
Put it on one repository
Connect GitHub or GitLab, enable a repo, and the next pull request opened gets a review.