How to Use Kilo Code AI Agent to Refactor a Large Codebase
Refactoring a large legacy codebase is one of the riskiest jobs in software development, but AI coding agents have made it far more approachable. Kilo Code is an open-source agent that understands your entire repository, letting you plan and execute safe refactors across many files without breaking existing behavior.
This guide shows you how to use Kilo Code to analyze, plan, and apply a large-scale refactor in VS Code with confidence.
Step-by-Step Guide
1. Install Kilo Code in your IDE
Install the Kilo Code extension in VS Code (or your preferred supported IDE) and sign in or connect your model provider. Grant it access to the current workspace so it can index your repository structure and dependencies.
2. Start in Architect mode to plan
Before touching code, switch to Architect Mode and describe your goal – for example, “break this monolithic service into smaller modules and remove the global state”. Kilo Code will inspect the codebase and propose a structured plan with the files and steps involved.
3. Review the plan before acting
Have the agent list affected files, dependencies, and risks. This review step is where you catch architectural problems early and confirm the refactor aligns with your team’s direction before any code changes are made.
4. Execute the refactor in Code mode
Once the plan is approved, switch to Code Mode and ask Kilo Code to implement it file by file. Because it has full-repository context, it can update imports, call sites, and tests across the codebase rather than only editing the function in front of you.
5. Validate with tests and code review
Run your test suite and ask the agent to generate or update tests for the changed paths. Use its built-in code review workflow to check for regressions and edge cases before merging your pull request.
The key to safe refactors with an AI agent is the division of labor between planning and execution. Use Kilo Code’s Architect mode to produce a reviewable plan, then Code mode to apply it with full-repository awareness, and always validate with tests and a code review pass.
By keeping the agent focused on clearly scoped goals and reviewing its output at each stage, you can modernize large codebases with far less risk – and much less manual file-by-file grunt work.
