Rewrite Java/Spring codebases to Python with spec-first, test-gated parity

If you’re asking “how do I rewrite Java/Spring to Python,” our answer is: extract executable specs, generate parity tests, implement in parallel, and roll out behind tests. With the LEAP Protocol, we begin by extracting interface and domain specifications from your Spring Boot app: OpenAPI from Spring MVC/WebFlux annotations, contract models from Jackson/Bean Validation (JSR 380), and schema/behavior from Spring Data JPA/Hibernate. From those specs we generate a comprehensive test suite (HTTP contract tests, property-based invariants, database fixtures, and message/cron semantics). In parallel, we implement your Python service stack—typically FastAPI/Starlette + Uvicorn, SQLAlchemy 2.x + Alembic, and Pydantic v2—with agents and engineers working against the generated tests. Finally, we ship behind parity: run shadow traffic, diff responses/events, dual-write/read as needed, canary the cutover, and deprecate the Java service only when tests and live comparators are green. This keeps business behavior stable while moving from Spring to a modern, lightweight Python runtime without pausing delivery.

Why Python for Java/Spring migrations

Lean, fast startup for services and functions: avoid JVM warm-up and classpath initialization; Uvicorn workers start quickly for containerized and serverless deployments.
First-class async I/O without Reactor: Python’s asyncio + FastAPI make concurrent HTTP, gRPC, and Kafka consumers straightforward with less framework ceremony.
Easier ML/analytics integration: direct access to NumPy/Pandas/Polars, scikit-learn, and PyTorch without cross-language bridges.
Type hints with runtime validation: Pydantic v2 enforces request/response schemas similarly to Bean Validation + Jackson, with clear error models.
Simpler deployment surfaces: small container images, straightforward packaging via uv/Poetry and wheels, and minimal JVM tuning.

Our methodology

1) Extract executable specs from Spring - Controllers: derive OpenAPI/JSON Schema from @RequestMapping/@RestController (MVC) or WebFlux annotations; map path/query/header/body and media types. - Models and validation: translate Jackson views/serializers and Bean Validation (e.g., @NotNull, @Pattern) to Pydantic field constraints and custom validators. - Persistence: introspect JPA/Hibernate entities and mappings (relations, cascades, fetch = LAZY/EAGER) into SQLAlchemy 2.x models and Alembic migration plans. - Security: map Spring Security configs (form login, OAuth2/OIDC resource server, method security) to FastAPI dependencies, auth backends, and scopes. - Integration: capture Kafka/JMS topics, schedulers (@Scheduled), and external clients (RestTemplate/WebClient) as message and job specs.

2) Generate parity tests - HTTP contracts: golden tests from OpenAPI with example payloads, status codes, and error envelopes; strict header and content-type checks. - Property-based invariants: Hypothesis-driven tests for idempotency, pagination, sorting, and validation boundaries mirroring Java constraints. - Data fidelity: snapshot tests from a representative database state (H2/PostgreSQL/MySQL) verifying SQL, indexes, and cascade/transaction behavior. - Messaging and jobs: tests for Kafka partition keys, headers, at-least/at-most-once semantics, and cron timings equivalent to @Scheduled.

3) Implement the Python target in parallel - Web: FastAPI/Starlette endpoints, dependency injection, and exception handlers to mirror Spring MVC/WebFlux behavior. - Models and DB: SQLAlchemy 2.x ORM or Core for explicit SQL where JPA proved leaky; Alembic migrations; careful session scoping to match @Transactional intent. - Validation/serialization: Pydantic v2 models, custom encoders to match Jackson date/time and enum formats; orjson/uvicorn-gunicorn for performance. - Async tasks: Celery/RQ or asyncio-native workers; aiokafka for Kafka; httpx for outbound HTTP with retries/backoff. - Tooling: type hints + mypy/pyright, pytest with the generated suite, coverage gates, and CI setup.

4) Roll out with test-gated parity - Staging replay: feed production logs to both services, diff responses and DB effects. - Shadow traffic: route a copy through Python via Envoy/NGINX, record mismatches with structured diagnostics. - Dual-write/dual-read as needed: gradually flip reads to Python while maintaining consistent writes; verify eventual consistency windows. - Canary and cutover: percent-based rollout, SLO monitoring, instant rollback, and retirement of the Java path only after continuous green.

5) Sustain and de-risk - Observability: OpenTelemetry tracing/metrics/logs equivalent to Spring Actuator insights. - Governance: pin dependencies, supply SBOMs, and adopt renovate-like updates; align Python runtime policy with your JVM-era controls.

Specific Java/Spring concerns we handle

Transaction and session boundaries: mapping @Transactional propagation/isolation to SQLAlchemy sessions; preventing accidental autocommit and N+1 caused by lazy loads.
Bean Validation ↔ Pydantic: precise translation of nullability/Optionals, regex patterns, size/range, and custom validators; consistent error messages and HTTP codes.
Jackson ↔ Pydantic/encoders: date/time (JSR-310) formats, BigDecimal precision, enum casing, and unknown-property handling.
Request binding differences: Spring’s @PathVariable/@RequestParam/@ModelAttribute and type conversion vs FastAPI’s parameter parsing; multipart/form-data uploads.
Security parity: Spring Security filters, method-level @PreAuthorize, BCrypt password hashing, and OAuth2/OIDC scopes mapped to FastAPI middlewares and backends.
Reactive to async: WebFlux/Reactor backpressure and cancellation semantics mapped to asyncio patterns without deadlocks or starvation.

Proof

sqlite-leap — one spec, five SQLite engines: https://github.com/safitudo/sqlite-leap
semver-leap — 5,632 passing tests from a single spec: https://github.com/safitudo/semver-leap
LEAP Protocol (spec-first, test-gated, agent-agnostic): https://github.com/safitudo/leap

These public repos demonstrate spec extraction, generator discipline, and test-gated delivery—the same mechanics we apply to Java/Spring → Python migrations.

Pricing & timeline

Typical engagement: 4–12 weeks depending on surface area, integrations, and data migration complexity.
Commercial model: fixed-price proposal after a 3–5 day discovery/spec audit.
Typical budget: $40,000–$120,000 for a service or bounded subsystem; larger monoliths can be phased by bounded contexts.

Ready to de-risk your Java/Spring rewrite to Python? Email hello@leapagentic.com with a repo link and a high-level inventory (endpoints, data stores, queues). We’ll run a fast spec audit and return a fixed-price, test-plan-anchored proposal.

Leap Agentic is distinct from Legacyleap.ai and Impetus Leap AI.