Review Memory

Review Memory is Optibot’s learning system. Every time a pull request is merged, Optibot analyzes the review it left and extracts the most important patterns — things like “when changes touch the payments module and modify error handling, missing rollback logic is common.” Those patterns are stored and automatically applied the next time a similar PR comes in.

Over time, Optibot builds a repository-specific knowledge base that makes reviews progressively more accurate and relevant to how your team writes code.

How it works

After a PR is merged, Optibot looks at the feedback it left and identifies which issues were actually addressed before the merge. Only feedback that was acted on is learned from — comments the author ignored are not.

From that feedback, Optibot extracts patterns describing recurring problems specific to your codebase and stores them as memories.

What gets extracted: actual bugs, security issues, missing error handling, and other breaking problems.
What does not get extracted: style preferences, naming conventions, formatting, or subjective feedback.

During future reviews, Optibot checks whether any stored patterns are relevant to the incoming PR. If a match is found, it uses those patterns to inform its review — but only flags something if it finds concrete evidence for it in the actual code changes. Patterns are never speculatively applied.

Memories gain confidence the more they’re observed, so patterns that appear repeatedly across multiple PRs carry more weight than one-off findings.

Managing memories

You can view and manage your repository’s memories from the Optibot dashboard at agents.getoptimal.ai → select a repository → Review Memory tab.

Viewing memories

Memories are listed with their pattern description, how many times they’ve been observed, their confidence score, and when they were last seen.

Editing a memory

Click any memory to edit its description or the problem it captures. Once you’ve edited a memory, Optibot will never automatically overwrite the text you’ve set — your version is preserved.

Pinning a memory

Pin a memory to make it more likely to surface during reviews. Use this for patterns that are especially important for your codebase — critical security issues, known architectural pitfalls, or problems your team frequently encounters.

Disabling a memory

Disable a memory to stop it from being used in future reviews without permanently removing it. Useful if a pattern has become outdated or no longer applies.

Deleting a memory

Delete a memory to permanently remove it. If the same pattern is observed again in future reviews, it will be re-created from scratch.

Frequently asked questions

Does Review Memory share data between repositories?
No. All memories are scoped to a single repository. Optibot does not apply patterns learned in one repo to another.

What happens if I edit a memory?
Optibot will continue to track new observations and update the memory’s confidence, but the description text you set will never be automatically overwritten.

Will Optibot always flag a pattern it has learned?
No. Memories inform the review — they don’t guarantee a comment. Optibot only surfaces a learned pattern if it finds concrete evidence for it in the current PR’s changes.

Does Review Memory work for GitLab as well?
Yes. Review Memory works the same way for both GitHub and GitLab pull/merge requests.