AI & RAG

Detect Breaking API Changes from Spec Diffs and Alert Owners

Compares the new OpenAPI spec against the previous version on each GitLab merge, uses retrieval over the changelog to classify whether changes are breaking.

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitLab merge touches an OpenAPI specGitLabGitLab
  • ActionFetch prior and new spec versions and diff themGitLabGitLab
  • ActionClassify deltas as breaking using changelog retrievalOpenAI
  • LogicBranch on whether breaking changes exist
  • OutputAlert endpoint owners in Slack with summarySlack

What it does

When a spec changes in GitLab, this pipeline diffs the new OpenAPI document against the prior version, identifies removed or altered operations and required fields, cross-references the changelog via retrieval to confirm intent, and classifies each change as breaking or safe. If anything is breaking, it sends a targeted alert with an explanation.

When to use it

Use it to catch accidental breaking changes before consumers do. Ideal for platform teams publishing APIs to many internal clients who need early, specific warnings rather than a wall of diff noise.

How it works

  1. 1A GitLab merge touching the spec triggers the flow.
  2. 2The previous and new spec versions are fetched and structurally diffed.
  3. 3Retrieval over the changelog grounds an OpenAI call that classifies each delta as breaking or safe.
  4. 4A branch checks whether any breaking changes were found.
  5. 5If breaking, a summary with affected endpoints is sent to the owners' Slack channel; if safe, the run ends quietly.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitLabRepos, MRs, pipelines, registry.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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