What is SQL to SQLAlchemy Converter?

SQLAlchemy has been the de facto standard for Python database interaction since 2006, powering everything from small Flask side projects to large-scale data platforms. Its declarative mapping system lets you define database tables as Python classes where each class attribute corresponds to a Column with an explicit type, constraint, and optional default value. This converter reads your raw CREATE TABLE statements and emits complete SQLAlchemy model classes using the declarative_base pattern, including proper Column() definitions with type parameters preserved, ForeignKey references translated into both Column-level ForeignKey() arguments and model-level relationship() properties, and dialect-specific imports for PostgreSQL features like JSONB and UUID or MySQL types like TINYINT and YEAR. The generated code follows the conventional import structure and is ready to paste into any project that uses SQLAlchemy 1.4 or 2.0.

How to Use

Choose your target database dialect from the dropdown — PostgreSQL, MySQL, or SQLite — to get the correct dialect-specific imports
Paste your CREATE TABLE statements into the left-hand input panel
Click Generate SQLAlchemy Model to produce the Python class definitions
Review the output, which includes import statements, the declarative Base, and all model classes with relationships
Copy the result into a models.py file within your Python project

Why Use This Tool?

Translates every SQL column constraint — PRIMARY KEY, NOT NULL, UNIQUE, DEFAULT, REFERENCES — into its SQLAlchemy equivalent

Generates relationship() properties alongside ForeignKey columns so you can traverse associations using Python attribute access instead of manual joins

Handles dialect-specific types automatically: JSONB and UUID for PostgreSQL, TINYINT and YEAR for MySQL

Preserves type parameters such as VARCHAR(255) as String(255) and NUMERIC(10,2) as Numeric(10, 2)

Converts SQL default functions like NOW() and CURRENT_TIMESTAMP into SQLAlchemy func.now() expressions

Tips & Best Practices

SERIAL columns receive both primary_key=True and autoincrement=True, matching PostgreSQL identity column semantics
The generated relationship() uses backref for bidirectional navigation — you can customize this to back_populates for explicit two-way relationships
For PostgreSQL-specific types like JSONB and UUID, the converter adds the appropriate from sqlalchemy.dialects.postgresql import line automatically
Consider adding __repr__ methods to the generated models for better debugging output in your application logs

Frequently Asked Questions

How are SQL column types translated to SQLAlchemy Column types?

The converter maps INTEGER to Integer, BIGINT to BigInteger, VARCHAR to String (with length parameter), TEXT to Text, BOOLEAN to Boolean, TIMESTAMP to DateTime, TIMESTAMPTZ to DateTime(timezone=True), DATE to Date, NUMERIC to Numeric (with precision and scale), FLOAT to Float, UUID to UUID, JSONB to JSON, and BLOB to LargeBinary. Type parameters like VARCHAR(100) are preserved as String(100).

When should I avoid using the SQLAlchemy ORM and use the Core layer instead?

If you need bulk insert performance exceeding millions of rows per second, fine-grained control over generated SQL, or stateless query construction without session management, SQLAlchemy Core is a better fit. The ORM adds identity map overhead and session lifecycle complexity that may not be worth it for ETL pipelines or reporting queries.

How does the converter handle foreign key relationships?

SQL REFERENCES clauses become two artifacts: a ForeignKey() argument on the Column definition pointing to the target table and column, and a relationship() property on the model class that enables Python-level navigation. For example, author_id INTEGER REFERENCES users(id) produces both a ForeignKey("users.id") column and an author = relationship("User", backref="posts") property.

Does the converter support SQLAlchemy 2.0 style mappings?

The generated code uses the declarative_base pattern compatible with both SQLAlchemy 1.4 and 2.0. For a fully modern 2.0 approach, you may want to migrate to the DeclarativeBase class and mapped_column() syntax, which this converter does not yet produce but which can be adapted from the output with minimal changes.

Is my SQL data sent to any external server?

No. All parsing and code generation runs entirely in your browser. No part of your database schema is transmitted over the network at any point during the conversion process.

Can I use the output with Flask-SQLAlchemy or FastAPI?

Yes. For Flask-SQLAlchemy, replace Base = declarative_base() with db = SQLAlchemy(app) and inherit from db.Model instead of Base. For FastAPI, the generated models work directly with SQLAlchemy sessions, and you can create separate Pydantic schemas for request/response validation alongside the ORM models.

Real-world Examples

Building a multi-tenant SaaS platform with SQLAlchemy

A SaaS application isolates customer data using tenant IDs on every table. The developer converts the existing SQL schema to SQLAlchemy models, then adds a custom query filter that automatically scopes all queries to the current tenant.

Data warehouse schema with PostgreSQL-specific types

An analytics platform stores event data with JSONB payloads and UUID identifiers. The converter generates models with the correct PostgreSQL dialect imports.

SQL to SQLAlchemy Converter