Best AI Agents for Refactoring Legacy Code

Legacy refactoring is where a coding agent has to prove that it understands more than the file currently open in front of it. The useful agent is the one that can map dependencies, explain the current behavior, make a bounded plan, change several files, and run the existing tests before asking for approval.

Our top pick: Claude Code

Claude Code is the best fit for large or unfamiliar repositories. It works from the terminal, so it can inspect the project structure, follow references across files, and keep the refactor close to the build and test commands your team already uses.

Use plan mode before allowing a broad change. Ask it to identify the files it expects to touch, the behavior that must not change, and the tests that prove the migration is safe. The main trade-off is that the terminal workflow asks more of the reviewer than an IDE with a built-in visual diff.

Best for a visible review loop: Cursor

Cursor is a strong option when a developer wants to stay inside an editor while refactoring. It is easier to review small transformations inline, compare the before and after, and stop after one safe slice of the work.

Choose Cursor for incremental cleanups, API migrations that can be split into small steps, and teams that want developers to approve changes close to the code. For a repository-wide migration, you still need to provide clear boundaries and run the full test suite yourself.

Best for delegated work: Devin

Devin is useful when the refactor can be expressed as a clear ticket with a known acceptance test. It can suit teams that want to delegate a bounded cleanup and review the resulting pull request later.

Do not start with a vague instruction such as “modernize this codebase.” Define the target behavior, the files or package boundary, the commands that must pass, and the changes that are out of scope.

A safer refactoring workflow

1. Create a test or characterization check for the behavior you need to preserve.
2. Ask the agent for a plan and a list of affected files before editing.
3. Work in small slices and run the existing tests after each slice.
4. Review the diff for accidental API changes, deleted edge-case handling, and new dependencies.
5. Keep the final merge decision with a human who understands the system.

For most teams, the right choice is the agent that makes the review loop safest, not the one that promises the most autonomous code generation.