Coauthored by Jonathon Frost, Aditya Duvuri and Shriram Muthukrishnan
More and more organizations are choosing PostgreSQL over proprietary database platforms such as Oracle, and for good reasons. It’s fully open source and community supported with a steady pace of innovation. It’s also preferred by developers for its extensibility and flexibility, often being used for vector data along with relational data to support modern applications and agents. Still, organizations considering a shift from Oracle to PostgreSQL, may hesitate due to the complexity that often accompanies an enterprise-scale migration project. Challenges such as incompatible data types, language mismatches, and the risk of breaking critical applications are hard to ignore.
Recently, the Azure Postgres team released a new, free tool for migrations from Oracle to PostgreSQL that was designed to address these challenges, making the decision to migrate a lot less risky. The new AI-assisted Oracle-to-PostgreSQL migration tool, available in public preview via the PostgreSQL extension for Visual Studio Code, brings automation, validation, and AI-powered migration assistance into a single, user-friendly interface.
Meet your new migration assistant
The AI-assisted Oracle to PostgreSQL migration tool dramatically simplifies moving off Oracle databases. Accessible through VS Code, the tool uses intelligent automation, powered by GitHub Copilot, to convert Oracle database schemas and PL/SQL code into PostgreSQL-compatible formats. It can analyze Oracle schema, and automatically translate table definitions, data types, and even stored procedures/triggers into PostgreSQL equivalents speeding up migrations that once took months of manual effort.
By handling the heavy lifting of schema and code conversion, this tool allows teams to focus on higher-level testing and optimization rather than tedious code rewrites. Users are already reporting that migrations are now faster, safer, and more transparent. The tool is simple, free, and ready for you to use today. Let’s take a look at how it works by covering the following:
- Creating the migration project
- Setting up the connections
- AI-assisted schema migration
- Reviewing schema migration report
- AI-assisted application migration
- Reviewing application migration report
Step by step with the AI-assisted Oracle-to-PostgreSQL migration tool
Step 1 – Create the project in VS Code
Start by installing or updating the PostgreSQL extension for VS Code from the marketplace. Open the PostgreSQL extension panel and click “Create Migration Project.” You’ll name your project, which will create a folder to store all migration artifacts. This folder will house extracted and converted files, organized for version control and collaboration.
Step 2 - Connect to your databases and AI model
Before beginning the migration, you’ll need to connect to the Oracle databases and select an OpenAI model to leverage during the process. Enter the connection details for your source Oracle database, credentials, and the schema to migrate. Then, select a PostgreSQL scratch database. This temporary environment is used to validate converted DDL in real time. Next, you will be prompted to select an OpenAI model.
Step 3 – Begin schema migration
Once you’ve set up your connections, click the button to start the schema migration. The tool performs an extraction of all relevant Oracle database objects: tables, views, packages, procedures, and more. The extracted DDL is saved as files in your project folder. This file-based approach functions like a software project, enabling change tracking, collaboration, and source control.
Enter - AI assistance
This is where the AI takes over. The tool breaks the extracted schema into manageable chunks, and each chunk is processed by a multi-agent orchestration system:
- The Migration Specialist Agent converts Oracle DDL to PostgreSQL.
- The Migration Critic Agent validates the conversion by executing it in the PostgreSQL scratch database.
- The Documentation Agent captures follow up review tasks, metadata, and coding notes for later integration with the application code migration process.
Each chunk is converted, validated, and deployed. If validation fails, the agents auto correct and retry. This self-healing loop ensures high conversion accuracy. Essentially, the tool conducts compile-time validation against a live PostgreSQL instance to catch issues early and reduce downstream surprises.
Checkpoint - review the schema migration report
Some complex objects, like Oracle packages with intricate PL/SQL, may not convert cleanly on the first pass. These are flagged as “review tasks.” You can invoke GitHub Copilot’s agent mode directly from VS Code to assist. The tool constructs a composite prompt with the original Oracle DDL, the partially converted PostgreSQL version, and any validation errors. This context-rich prompt enables Copilot to generate more accurate fixes.
With the schema fully converted, you can compare the original Oracle and new PostgreSQL versions side by side. Right-click any object in the project folder and select “Compare File Pair." You can also use the “Visualize Schema” feature to see a graphical representation of the converted schema. This is ideal for verifying tables, relationships, and constraints.
Once the schema migration is complete, the tool generates a detailed report that includes:
- Total number of objects converted
- Conversion success rate
- PostgreSQL version and extensions used
- List of converted objects by type
- Any flagged review tasks
This report serves as both a validation summary and an audit artifact. It helps confirm success and identify any follow-up actions. If you have compliance or change management requirements you need to meet, this documentation is essential.
Step 4 – Begin application migration
The next phase that the tool supports is updating the application code that interacts with the schema. Migrations often stall when code is overlooked or when traditional tools treat SQL statements as simple strings rather than part of a cohesive system. The AI-assisted Oracle-to-PostgreSQL migration tool’s application conversion feature takes a more holistic, context-aware approach.
Before starting, you’ll need to configure GitHub Copilot Agent Mode with a capable AI model. Then, navigate to the ‘application_code’ directory typically found in .github/postgres-migration/<project_name>/application_code, and copy your source code into this directory. Keeping your application and converted schema together provides the AI with the structural context it needs to refactor your code accurately. To start the app migration, this time you’d select the "Migrate Application" button. Then select the folder containing your source code and the converted schema.
Enter - AI assistance
The AI orchestrator will analyze your application’s database interactions against the new Postgres schema and generate a series of transformation tasks. These tasks address SQL dialect changes, data access modifications, and library updates. This process goes beyond a simple search-and-replace operation. The AI queries your migrated PostgreSQL database to gain grounded context of your converted schema, and ensures that things like function signatures, data types, and ORM models are migrated correctly in the application code.
Checkpoint - review the app migration report
When the AI finishes converting your application, it produces a detailed summary. The report lists which files were migrated, notes any unresolved tasks, and outlines how the changes map to the database schema. This audit-ready document can help DBAs and developers collaborate effectively on follow-up actions and integration testing.
You can use VS Code’s built-in diff viewer to compare each migrated file with its original. Right-click on a migrated file and select "Compare App Migration File Pairs" to open a side-by-side view. This comparison highlights differences in SQL queries, driver imports, and other code changes, allowing you to verify the updates.
Wrapping up the migration project
During schema migration, the tool created detailed coding notes summarizing data-type mappings, constraints, and package transformations. These notes are essential for understanding why specific changes were made and for guiding the application conversion. Use them as reference points when validating and refining the AI-generated application code.
Destination - PostgreSQL on Azure
The AI-assisted Oracle-to-PostgreSQL migration tool brings together automation, validation, and AI to make Oracle-to-PostgreSQL migrations faster, safer, and more transparent. With schema extraction, multi-agent orchestration, app conversion, real-time validation, and detailed reporting, it provides a clear, confident path to modernization so you can start taking advantage of the benefits of open-source Postgres.
What’s in store
On the other side of a successful migration project to PostgreSQL on Azure, you get:
- First-class support in Azure
- Significantly lower total cost of ownership from eliminating license fees and reducing vendor lock-in
- Unmatched extensibility, with support for custom data types, procedural languages and powerful extensions like PostGIS, TimescaleDB, pgvector, Azure AI, and DiskANN
- Frequent updates and cutting-edge features delivered via a vibrant open-source community
Whether you’re migrating a single schema or leading a broader replatforming initiative, the AI-assisted Oracle-to-PostgreSQL migration tool helps you move forward with confidence without sacrificing control or visibility.
Microsoft