How to migrate a legacy codebase with an AI coding tool
Step-by-step approach for using AI to migrate large, old codebases without breaking everything.
Recommended tools
- 🤖Claude Code
1M-token context handles entire legacy systems in one go.
- 🧰Aider
Git-native edits let you safely revert any migration step.
- 🟦Cursor
Composer plus Background Agents is a good balance for gradual migration.
Legacy migrations are the highest-value, highest-risk AI coding tasks. A single bad auto-edit can propagate across hundreds of files. The trick is picking a tool with strong verification primitives and a migration pattern that keeps humans in the loop at the boundaries.
Our playbook: start by asking the agent to write a migration plan as a markdown file. Review it. Then have the agent execute the plan one module at a time, running tests between each module. Reject any change where the test suite doesn't stay green.
Claude Code's 1M-token window makes it the best fit for step one — it can read the whole codebase and produce a coherent plan. For step two, Aider's git-tracked edits are a safety net. Cursor's Composer works too, but you'll want to break it into smaller batches to stay within context.
Budget rule: expect a mid-sized migration (20-50k lines) to cost $40-$200 in API fees depending on model choice. Open-weight models cut this roughly 80% at the cost of some quality.