SUMMARIZATION

Sentry release-health digest into the deploy's GitLab MR

On each new Sentry release, summarizes new error signatures and crash-rate regressions and posts a plain-English health digest as a comment on the GitLab merge request…

CategorySummarization
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSentry new release createdSentrySentry
  • ActionFetch release issues and crash-free rateSentrySentry
  • ActionSummarize new and regressed signaturesOpenAI
  • LogicMatch release commit SHA to its GitLab MR
  • OutputPost health digest as MR commentGitLabGitLab

What it does

When Sentry registers a new release, this workflow pulls the issues and session crash-rate tied to that release, summarizes what is new or regressed versus the prior release, and drops a readable digest comment on the GitLab merge request that produced the deploy.

When to use it

Use it when your team merges to deploy and wants release health surfaced where code review already happens, instead of buried in the Sentry dashboard. It turns a noisy issue stream into a single "is this release healthy?" verdict on the MR.

How it works

  1. 1Sentry fires a release-created webhook with the version and associated commits.
  2. 2The flow fetches new and regressed issues plus the crash-free session rate for that release from Sentry.
  3. 3An OpenAI summarization step groups errors by signature, flags first-seen vs. regressed, and writes a concise health summary with a healthy/degraded verdict.
  4. 4The release version is matched to its GitLab MR via the commit SHA.
  5. 5The digest is posted as a comment on that MR so reviewers see it inline.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect GitLabRepos, MRs, pipelines, registry.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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