AI & RAG

Ground MR Review Comments in ADR Rationale

When a GitLab merge request is opened, retrieves the most relevant architecture decision records and posts a review comment that cites which decisions the change touches and why.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitLab MR opened or updatedGitLabGitLab
  • ActionFetch MR description and changed pathsGitLabGitLab
  • ActionRetrieve candidate ADR docsConfluenceConfluence
  • ActionSelect relevant ADRs and draft cited summaryOpenAI
  • OutputPost grounded review comment to MRGitLabGitLab

What it does

Every time a merge request is opened or updated, this bot reads the MR title, description, and changed file paths, then searches your ADR corpus for the decisions most relevant to the change. It posts a single grounded review comment that names the specific ADRs in play, quotes the rationale behind each, and flags where the diff appears to diverge from a recorded decision.

When to use it

Use it on repos where architecture decisions are documented as ADRs but reviewers keep re-litigating settled questions. It gives every reviewer the decision context up front instead of forcing them to dig through wiki history.

How it works

  1. 1A GitLab webhook fires on merge_request open/update.
  2. 2The flow pulls the MR description and changed paths from the GitLab API.
  3. 3ADR documents are fetched from Confluence and embedded for retrieval.
  4. 4An OpenAI call selects the top decisions and drafts a citation-backed summary, including any apparent divergence.
  5. 5The comment is posted back to the MR thread with linked ADR references.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitLabRepos, MRs, pipelines, registry.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  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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