OmniMigrate

Inspiration

With the rise of AI coding assistants, code generation has accelerated, but codebase maintainability has sharply declined. Developers face an "AI Technical Debt Crisis": outdated dependencies, deprecated framework APIs, and untested legacy modules. Upgrading legacy systems (such as migrating from Python 3.8/Django 2.2 to Python 3.12/Django 5) usually stalls feature development for months. We wanted to build an asynchronous, controlled "Migration Factory" that automates heavy code refactoring while guaranteeing that business logic never breaks in production. What it does

OmniMigrate is an asynchronous, multi-layer code modernization pipeline for Python projects. Instead of interactive real-time inline completions, it operates as a background engine:

Parses & Maps: Builds concrete syntax trees (using LibCST) to map dependencies and execute deterministic structural changes.

Locks Behavior: Auto-generates characterization tests to capture baseline runtime behavior before refactoring.

Refactors with AI: Leverages NVIDIA Nemotron models via Nebius Token Factory for complex syntax upgrades and logic adaptations.

Validates in Sandbox: Runs test suites inside an isolated, network-disabled Docker sandbox.

Self-Heals: Detects test failures, analyzes tracebacks, and applies targeted fixes without weakening original assertions.

Delivers Auditable PRs: Generates complete GitHub Pull Requests for human review (Human-in-the-Loop).

How we built it

Core & Pipeline: Built with Python (FastAPI) and LibCST for deterministic AST manipulation.

AI Infrastructure: Integrated with Nebius Token Factory using OpenAI-compatible endpoints. We deployed NVIDIA Nemotron 3 Ultra for deep reasoning in our Self-Healing Loop and NVIDIA Nemotron Nano/Super for fast structural parsing.

Sandboxing & Security: Created an isolated Docker execution environment with strict CPU/memory/timeout limits and zero network access to execute test suites securely.

Integrations: GitHub API for automated branch management, diff generation, and Pull Request creation.

Challenges we ran into

Preventing "Shortcut" Self-Healing: Early LLM prompts attempted to fix failing tests by altering or removing assertions. We had to enforce strict AST-level read-only protections on test files.

Managing Non-Deterministic Output: LLMs can introduce subtle syntax regressions. We solved this by combining deterministic AST transformations for standard replacements with LLMs only for complex logic shifts.

Preserving Context: Feeding entire repositories into an LLM exceeds context windows and increases latency. Using LibCST allowed us to isolate and feed only relevant module sub-trees.

Accomplishments that we're proud of

Zero-Ghallucination Verification: Built a 4-level verification framework (Structure, Behavior, Security, Traceability) where every code change must pass green tests in a sandbox.

High-Speed Self-Healing: Successfully integrated NVIDIA Nemotron 3 Ultra on Nebius Token Factory to diagnose traceback errors and fix broken dependencies autonomously.

VPC & On-Premise Readiness: Architecture allows source code to remain fully inside private networks or local GPU deployments.

What we learned

Determinism > Pure Generation: AI agents are vastly more effective when anchored by deterministic tools (AST trees) rather than raw prompt engineering.

Preserving Existing Logic is Key: Locking down baseline behavior with characterization tests before refactoring is essential for building trust with senior engineers and CTOs.

What's next for OmniMigrate

Expanded Framework Support: Extend our specialized migration recipes to FastAPI, Pydantic v1-to-v2, and AsyncIO transformations.

Enhanced Test Generation: Improve our AI-driven characterization test generator to cover low-coverage legacy modules automatically.

Self-Hosted Enterprise Runner: Package OmniMigrate into a single-command Helm Chart / Docker Compose runner for air-gapped enterprise VPC environments.