AI-Powered Code Migration Between Languages
We often receive legacy projects where Python backend needs to be migrated to TypeScript, Java to Kotlin. Rewriting manually is expensive and time-consuming: a 15,000-line project takes 3–4 months, with budgets running into tens of thousands of dollars. Transpilers like Babel or j2objc produce unreadable code that ignores idioms: snake_case remains snake_case, and Pydantic models turn into bulky classes. We built an LLM-based system that doesn't just translate syntax but adapts architecture: it replaces libraries with idiomatic equivalents, converts types, and validates the result. In this article, we share the architecture and a real case of migrating a notification service from Python FastAPI to TypeScript. With over 30 successful migrations and 5 years in the field, we guarantee results.
Why AI Migration Is More Efficient Than Manual Refactoring?
Manual code migration is a task that takes weeks and months. AI migration cuts time by 5–7x for projects up to 15,000 lines. The resulting code is idiomatic: snake_case becomes camelCase, Pydantic models become Zod schemas, and async calls become native Promises. Our experience shows that 70–85% of lines are migrated without edits; the remaining 15% is complex business logic that we refine manually. Budget savings reach 50–70% compared to manual refactoring. For a typical 15K-line project, manual migration costs $30k-$60k, while AI migration costs $10k-$20k.
| Criteria | Manual Migration | AI Migration |
|---|---|---|
| Time (15K lines) | 3–4 months | 3–6 weeks |
| Code idiomaticity | Depends on developer | Guaranteed by glossary |
| Compilation errors | Many, fixed manually | Auto-fix via LLM |
| Cost | High (expensive senior hours) | 50–70% cost reduction |
How Is the AI Migration Architecture Designed?
The naive approach—feeding the LLM the entire file and asking for a translation—works only for files up to 200–300 lines. For real codebases, we use a four-component architecture:
- Dependency Analyzer — builds a dependency graph between modules and determines the migration order. Uses AST to analyze imports.
- Chunk Splitter — splits files into independent chunks (classes, functions, modules) that can be migrated and tested in isolation.
- Context Manager — passes already migrated dependencies to the LLM so that new files use correct imports.
- Validator — compiles and tests the migrated code; on errors, triggers auto-fix.
A key element is the Glossary: a dictionary mapping source language libraries to target language equivalents. For example, Pydantic → Zod, SQLAlchemy → Prisma, FastAPI → Express+Hono.
Example glossary configuration for Python→TypeScript
---
# source: Python, target: TypeScript
# pydantic.BaseModel: zod.ZodObject
# sqlalchemy.orm.Session: prisma.PrismaClient
# fastapi.FastAPI: express.Application
---How We Migrate Your Project: Stages
- Analysis and glossary preparation — we study the source codebase, build a dependency graph, configure library mappings. Takes 1–2 days.
- Migration of isolated modules — first we migrate models and utilities, then services and routes. Each file is compiled and tested separately.
- Integration and auto-fix — we merge migrated modules, run a full build. On compilation errors, the system automatically fixes them via LLM.
- Testing — we run unit tests (Jest/Vitest) and integration tests. If coverage drops more than 10%, we manually refine tests.
- Delivery — we provide the code, glossary documentation, coverage report, and instructions for re-migration.
Practical Case: Python Microservice → TypeScript
Context: A startup migrated a notification service (Python FastAPI, 3200 lines) to TypeScript to unify the stack (the frontend team knew only JS/TS).
Scope: 28 files, 12 Pydantic models, 34 API endpoints, 180 unit tests. Process (2 weeks):
- Week 1: glossary setup, migration of models and utilities (automatic), manual refinement of 3 complex files with business logic.
- Week 2: migration of routes, test adaptation (Jest), integration testing.
Results:
- 85% of code migrated automatically without manual edits.
- 15% required refinement (complex logic with Python-specific idioms).
- TypeScript compilation errors: 47 → 0 (after 2 LLM fix iterations).
- Test coverage of the migrated service: 71% (was 74% in Python — minimal loss).
- Unexpected bonus: during migration, AI identified 3 spots with potential race conditions in Python code, which were fixed in the TypeScript version.
We expected automation only for simple files, but the system handled 85% of the code—this saved us 3 weeks of manual work. — noted the startup engineer.
What Is Included in the Migration Result?
We deliver a complete package:
- Migrated code in the target language (entire codebase).
- Glossary documentation (library mappings).
- Documentation for the migrated code.
- Configured compilation and testing pipeline.
- Test coverage report.
- Access to the migration tool.
- Training session for your developers.
- One month of post-migration support.
- Instructions for re-migration upon updates.
Each migrated file is compiled with the strict flag. On errors, auto-fix via LLM is triggered. After all files are migrated, unit tests (Jest) and integration tests are run. If coverage drops more than 10%, we manually refine tests. The entire process is repeated until full success.
How Long Does Migration Take?
| Stage | Timeline | Work Share |
|---|---|---|
| Prototype of one file | 1–2 days | 10% |
| Dependency Graph + batch | ~1 week | 30% |
| Validation + auto-fix | ~1 week | 30% |
| Full project migration | 3–6 weeks (with QA) | 30% |
Contact us for a consultation on codebase migration. Get an analysis of your project in 1–2 days and learn the exact time and budget savings. Request a consultation — we will analyze your project and propose the optimal approach.