Morph

Rather than manually hunting for every instance of a deprecated API call, an outdated pattern, or a renamed function across hundreds of files, developers can describe the transformation they want ('replace all uses of the old logger API with the new structured logging interface') and Morph identifies every affected location and generates the appropriate transformation for each context, respecting the different ways the pattern appears across different usage scenarios.

Morph's semantic transformation approach handles the contextual complexity that makes mechanical find-and-replace inadequate for real code changes: a function signature change that adds a required parameter needs different handling depending on where it's called and what the appropriate default value is in each context; an API migration may require different import changes in different files; a renamed class may have different alias usage across different modules.

These contextual differences require understanding code intent rather than just matching text patterns, and Morph's language model-backed transformation engine provides this semantic understanding at codebase scale.

The platform integrates with GitHub and GitLab to generate pull requests from planned transformations, allowing engineering teams to review proposed changes with the same code review process used for manually written code before applying them.

This review step is essential for transformations at scale, where the confidence that every automated change is correct matters more than when reviewing smaller, manually written changes.

For engineering teams managing large codebases through migrations, deprecation campaigns, and architectural refactoring, Morph provides the transformation execution layer that makes codebase-wide change a systematic, reviewable process rather than a months-long manual effort that inevitably leaves stragglers.