Help us improve Gearset
We love getting feedback from our users on how we can make Gearset even better. Post your ideas for improvements, new features, and bug fixes alike, and vote for others – let us know what’s important to you.
1456 results found
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Auto-delete stale and abandoned branches
It would be great to have an option to automatically delete branches (and their PRs, if there are any) that have had no activity for a configurable period, e.g., 3 months. This would help teams be more organized and efficient by reducing clutter. Currently, branches are deleted only when their PR reaches main or when both the PR and the branch are manually deleted. Stale or abandoned branches that never get promoted sit there and create a mess, and cleaning them up means manually checking for open PRs before deleting them in Git.
7 votes -
Auto-delete feature branches only after final deployment. Current deletion can hinder troubleshooting if pipeline promotion fails.
The current "Auto delete feature branches when there are no PRs open against the branch" setting works well for general cleanup. However, in pipeline-driven deployments, a branch can be deleted even if the change fails to progress to the next stage.
Consider adding an option to delete feature branches only after the deployment successfully reaches the final environment (e.g., Production). This would preserve troubleshooting visibility when pipeline promotion fails and help teams investigate root causes before the branch is removed.
6 votes -
Deleted field references doesn't get auto-cleaned up in Layouts and ReportTypes
If a custom field is deleted, its references in Layouts and ReportTypes are not getting deleted in the environment branches. So each time, developer had to edit all the report types and layouts to remove the deleted field references. Imagine, this being done for hundreds of reportTypes in the system. If this cleanup is automated using Gearset, it would be more helpful for the developers.
15 votes -
Automatically detect drift between the org and main git branch
Currently we can automatically monitor and report on changes made to an org via the Change Monitoring dashboard. This compares an org snapshot to the previous days snapshot.
However, what would be really useful now we have pipelines enabled is the ability to automatically report on org drift from our Github main branch, using a specified filter (i.e. the same filter as our Pipeline CI Jobs use). This would allow us to track, understand and hopefully reduce the amount of drift that occurs in pipeline-relevant objects, making the pipeline more robust.
19 votes -
Allow pre- and post-deployment steps for items that cannot be deployed through the Metadata API
We encounter a variety of user stories that require manual changes to items that cannot be deployed through the Metadata API. If these are the only changes required, there is no way to track these pre- and post-deployment steps, since a PR (and thus a metadata commit) is required to add the steps in Gearset.
2 votes -
Commit test class selection
When initially creating a commit, the only way to specify tests is to manually create a comma or new line separated list. If the validation fails, there is then an option to add tests with a nice searchable/selectable list before revalidating. Can this test class selection UI be added to the initial commit process?
3 votes -
Support for decomposed permission sets
Please add support for decomposed permission sets: https://developer.salesforce.com/docs/atlas.en-us.sfdx_dev.meta/sfdx_dev/sfdx_dev_ws_decomposed_md_types.htm. Permission set decomposition makes large permission sets much more manageable. Decomposed permission sets currently fail to deploy through Gearset. I confirmed with Gearset support that it isn't currently supported.
5 votes -
Validate configdata deployments
We are implementing RCA and storing the configdata in source control. Because it is a data deployment and not a metadata deployment, no validations run to ensure the data will deploy correctly. It would be great if there was some way to run validation for the data deployment before merging a pull request that merges it to the org as a check similar to metadata validation.
3 votes -
allow a second sandbox to pull PR changes before PR is promoted to main
Currently I have to manually apply a PR from another developer to my sandbox. It would be great if we could sync back a PR before it reached main or sync across from one sandbox to another
2 votes -
Delete feature branch ONLY after production deployment
The ability to delete feature branches only after merging with master branch (which runs the ci job to deploy to Production). Currently the feature branch will delete if there is no PR attached with it. There are instances where I may want to delete a PR to the 'QA' or 'Release' branch but still want to keep the feature branch. Currently this is not possible.
7 votes -
Quick Fetch Latest Changes
The quick fetch feature would just ensure that the components that already exist in that feature branch, are refreshed i.e. their latest version is fetched and automatically added to the commit with the click of a button, without having to go into the comparison view again, from which, now the user can create a new pull request.
2 votes -
Pay as you go Pricing
There has to be a pay as you go pricing model for Contractors who have random uptime and downtime in using Gearset.
2 votes -
Restrict the branch from the promotion if developer add any metadata type other then the metadata types on CI Job filter.
Currently Gearset is giving a warning on PR if metadata type added on the branch doesn't present in CI Job filter, instead if we a have something to restrict /block promotion it helps.
Scenario: Even if developer uses CI Job filter at time of comparison still, they will be able to compare additional metadata types, hence they commit those to the branch. At the PR level it is showing as warning message which developers are missing and promoting changes. After promoting they are coming back and asking why those haven't deployed even it is showing as deployment successful.
1 vote -
Multiple announcements in pipeline
Currently we are unable to add more than 1 announcement in the pipeline. If I want to make more than 1 announcement in pipeline that would help me and my team a lot. Requesting a new feature which allows us to add more than 1 announcement in the pipeline
1 vote -
Feature Request: Bulk / Mass-Selection for Filtered and Sorted Objects in Backup Settings
Problem & Pain Point
Configuring data backup jobs for large orgs is currently a highly manual and time-consuming process.While the Recommended objects filter is a helpful starting point, it unfortunately includes many objects that we do not care to back up, leading to unnecessary noise and storage bloat. To circumvent this, our goal is to only select objects that actually contain records.
While Gearset allows us to sort the table by the Records column to bring populated objects to the top, there is no way to mass-select them. Having to manually click hundreds of checkboxes one by one is…
1 vote -
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1 vote -
Upload a screen shot of the Flow Application error and get details on how to fix it
I saw a demo from Copado today where they are able to upload a screen shot of the Flow Application email into their AI agent and it spit out details on how to fix the error in the flow. Is this something Gearset offers or plans to offer in the future?
2 votes -
flow renamed element
When comparing Flows, we might be updating (API) names of Elements, according to our naming conventions.
Gearset then treats them as new on the source, and deleted on the right without linking them.It would be great if this can be improved, either automatic, or giving us the option to provide both API Names, so then the comparison can be rerun.
1 vote -
Add AWS Bedrock Integration for AI-Powered Deployment Reviews and Release Automation
As more teams adopt generative AI in their DevOps workflows, it would be valuable for Gearset to offer an AWS Bedrock integration.
Potential use cases include:
Generate AI-assisted deployment summaries before promoting changes.
Review metadata differences and highlight potential deployment risks.
Explain deployment failures with actionable recommendations.
Produce release notes automatically from deployment changes.
Help identify dependency or configuration issues before deployment.
Support organization-approved foundation models through Amazon Bedrock to meet security and compliance requirements.Many enterprises already use AWS services, so an AWS Bedrock integration would allow them to add AI capabilities without relying on external AI providers. It…
1 vote -
When we have the problem analyzer suggest to remove things (like fields) an option to also add that field would be great
Having the ability to add a field instead of just remove it et al in the problem analyzer would be sweet
1 vote
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