How do I refactor an Apache Airflow DAG without breaking it?

Atlas maps all public symbols and their callsites using `lsp findReferences`, then pins behavior with `pytest (DagBag)` before applying changes incrementally with `apply_patch`, re-running tests after each step to prevent regressions in your Apache Airflow DAGs.

Can Atlas help me move a database call out of Airflow DAG top-level code?

Yes, Atlas can identify database calls in your `dags/` files and, behind a permission prompt, rewrite the code to move such calls out of the DAG top-level, preventing constant re-parsing by the Apache Airflow scheduler and improving performance.

How does Atlas ensure my Apache Airflow tests pass after a refactor?

Atlas runs your existing `pytest (DagBag)` tests to establish a green baseline. After each `apply_patch` operation, it re-runs these tests using `uv run pytest --dagbag-import-tests`, ensuring that every structural change maintains the expected behavior of your Apache Airflow DAGs and tasks.

What Apache Airflow tools does Atlas integrate with for refactoring?

Atlas integrates directly with your Apache Airflow toolchain, including `pytest (DagBag)` for testing, `uv` for package management (pinning `apache-airflow` in `pyproject.toml`), and `ruff format` for consistent code formatting, all invoked via `atlas bash`.

How does Atlas handle code formatting in Apache Airflow files?

Atlas can invoke `ruff format` on any modified Apache Airflow file. It computes a unified diff for every edit and can apply `ruff format` to that diff, ensuring your `dags/` code adheres to styling standards before committing, preventing formatting regressions.

Can Atlas rewrite PythonOperators to use the TaskFlow API in Airflow?

Yes, Atlas can rewrite a chain of `PythonOperator` tasks within your Apache Airflow DAGs to leverage the TaskFlow API, transforming XCom passing into plain Python return values for cleaner, more idiomatic code, improving DAG readability and maintainability.

How does Atlas prevent accidental changes to my Apache Airflow codebase?

Atlas employs multiple safety measures: it drafts a plan in a read-only agent, every tool call is permission-gated, and it presents a unified diff for every file edit for your approval before writing any changes to your `dags/` or `pyproject.toml` files, ensuring control over your Apache Airflow project.

Refactor a Legacy Module in Apache Airflow with Atlas in 2026

Refactoring a legacy module in Apache Airflow without breaking existing DAGs or callers is a critical task, and Atlas streamlines this process by mapping public surfaces, pinning behavior with `pytest (DagBag)`, applying structural changes, and re-running tests after each modification, all while managing dependencies with `uv` and formatting with `ruff format`.

How to Map the Public Surface of an Apache Airflow Module for Refactoring

Mapping the public surface of an Apache Airflow module is the crucial first step in any refactor, ensuring no callsite is missed. Atlas achieves this by leveraging its `lsp` tool to perform `documentSymbol` operations, identifying all 15-20 exported functions and classes within your `dags/` folder, then finding every `findReferences` for each.

Before any code modification, Atlas uses its `lsp` tool to thoroughly understand the module's structure and its interactions within your Apache Airflow project. First, `atlas lsp documentSymbol` is run on the target Python file within your `dags/` directory. This operation provides a comprehensive list of all top-level declarations, such as DAG objects, custom operators, or utility functions. For each identified symbol, Atlas then executes `atlas lsp findReferences`. This crucial step enumerates every single callsite across your entire codebase, including other DAGs, task definitions, or helper modules. This ensures that when you restructure the module, you have a complete `todowrite` list of all locations that might require updates, preventing silent breakage of your Apache Airflow workflows.

Pinning Apache Airflow Module Behavior with `pytest (DagBag)` Before Refactoring

Pinning the behavior of an Apache Airflow module before refactoring is essential to prevent regressions, and Atlas facilitates this by running your existing `pytest (DagBag)` tests. This establishes a green baseline, ensuring that any subsequent structural changes do not alter the module's 100% expected output or DAG execution logic.

A successful refactor in Apache Airflow demands absolute certainty that the module's external behavior remains unchanged. Atlas ensures this by first establishing a 'green baseline' using your existing test suite. You instruct Atlas to execute your `pytest (DagBag)` tests via the `atlas bash` tool, using the command `uv run pytest --dagbag-import-tests`. This command is critical for Apache Airflow, as it not only runs unit tests but also validates the parsing and loading of your DAGs, catching issues in task dependencies or top-level code. Atlas records the output of these tests, providing a concrete, verifiable benchmark. Any deviation from this baseline after a change immediately signals a regression, allowing you to address it before it impacts your production Airflow environment.

Applying Structural Changes to Apache Airflow Code Incrementally with Atlas

Applying structural changes to an Apache Airflow module incrementally minimizes risk, and Atlas uses its `apply_patch` tool for this precise purpose. This method ensures that each hunk of code is applied against the expected context, failing with `Failed to find context` if the file has drifted, allowing for a controlled refactor of 5-10 lines at a time.

Once the public surface is mapped and behavior is pinned, Atlas proceeds with the actual restructuring using its `apply_patch` tool. This tool is designed for robust, context-aware code modifications. Instead of simply overwriting files, `apply_patch` seeks out the exact context lines and `old_lines` for each hunk of the proposed change. If the file has been modified externally or has drifted from the expected state, `apply_patch` will fail with a `Failed to find context` error, preventing unintended changes. This granular control is invaluable when refactoring complex Apache Airflow DAGs or custom operators, allowing you to move a database call out of DAG top-level code or rewrite a chain of `PythonOperator` tasks using the TaskFlow API with confidence. Crucially, after each successful `apply_patch` operation, Atlas re-runs the `pytest (DagBag)` tests to immediately verify that the small, incremental change has not introduced any regressions.

Ensuring Safety and Review in Apache Airflow Refactors with Atlas

Ensuring safety and review during an Apache Airflow refactor is paramount, and Atlas integrates multiple safeguards into its workflow. Every Atlas tool call is permission-gated, and it drafts a plan in a read-only agent before executing, providing 2-3 layers of approval before any changes are written to your `dags/` files.

Atlas is built with safety and developer control at its core, especially vital when modifying critical Apache Airflow infrastructure. Before any tool, such as `lsp`, `apply_patch`, or `bash`, is executed, Atlas checks against permission-gated allow, ask, and deny rules. This means you explicitly approve every action. Furthermore, Atlas drafts a comprehensive plan in a read-only plan agent, allowing you to review the proposed steps before any build agent begins making changes. For every file edit, Atlas computes a unified diff and surfaces it for your approval, ensuring transparency. It also reads `git` branches, status, and diffs, and can stage and create commits on your behalf, providing a complete audit trail. This multi-layered approval process ensures that your Apache Airflow codebase remains stable and that no changes are applied without your explicit consent.

Managing Apache Airflow Dependencies and Formatting During Refactoring

Managing Apache Airflow dependencies and formatting during a refactor is critical for maintaining code quality, and Atlas direct integrates with your existing toolchain. It ensures that `uv` manages your `apache-airflow` dependencies in `pyproject.toml` and `ruff format` applies consistent styling to every diff, preventing 0 formatting regressions.

Maintaining a clean and consistent codebase is essential, particularly in a collaborative Apache Airflow environment. Atlas integrates directly with your preferred Python toolchain to uphold these standards throughout the refactoring process. For dependency management, Atlas expects and works with `uv`, ensuring that your `pyproject.toml` file accurately pins `apache-airflow` and other project dependencies. for code style, Atlas can invoke `ruff format` on any modified file. After an `apply_patch` operation, Atlas can automatically apply `ruff format` to the generated diff, ensuring that all new or modified code adheres to your project's formatting standards. This prevents the introduction of formatting inconsistencies, keeping your `dags/` files and custom operators clean and readable, and reducing friction during code reviews.

Step by step

01Map the module's public surface: Use `atlas lsp documentSymbol` on the target Python file in your `dags/` folder, then for each identified symbol, run `atlas lsp findReferences` to enumerate all callsites.
02Pin behavior first: Execute your existing `pytest (DagBag)` tests using `atlas bash "uv run pytest --dagbag-import-tests"` to establish a green baseline for your Apache Airflow DAGs and tasks.
03Restructure with `apply_patch`: Instruct Atlas to apply structural changes to the module using `atlas apply_patch`, for example, to move a database call out of DAG top-level code.
04Re-run tests after each hunk: Immediately after each successful `atlas apply_patch` operation, re-verify behavior by running `atlas bash "uv run pytest --dagbag-import-tests"` again.
05Track remaining callsites: Use `atlas todowrite` to maintain a list of callsites identified by `findReferences` that still need migration or adjustment.
06Review and approve changes: Review the unified diff presented by Atlas using `atlas read`, then approve the changes and allow Atlas to stage and create a `git commit`.
07Format the modified files: Ensure code consistency by running `atlas bash "ruff format dags/my_module.py"` on the refactored Apache Airflow files.

Frequently asked questions

How do I refactor an Apache Airflow DAG without breaking it?: Atlas maps all public symbols and their callsites using `lsp findReferences`, then pins behavior with `pytest (DagBag)` before applying changes incrementally with `apply_patch`, re-running tests after each step to prevent regressions in your Apache Airflow DAGs.
Can Atlas help me move a database call out of Airflow DAG top-level code?: Yes, Atlas can identify database calls in your `dags/` files and, behind a permission prompt, rewrite the code to move such calls out of the DAG top-level, preventing constant re-parsing by the Apache Airflow scheduler and improving performance.
How does Atlas ensure my Apache Airflow tests pass after a refactor?: Atlas runs your existing `pytest (DagBag)` tests to establish a green baseline. After each `apply_patch` operation, it re-runs these tests using `uv run pytest --dagbag-import-tests`, ensuring that every structural change maintains the expected behavior of your Apache Airflow DAGs and tasks.
What Apache Airflow tools does Atlas integrate with for refactoring?: Atlas integrates directly with your Apache Airflow toolchain, including `pytest (DagBag)` for testing, `uv` for package management (pinning `apache-airflow` in `pyproject.toml`), and `ruff format` for consistent code formatting, all invoked via `atlas bash`.
How does Atlas handle code formatting in Apache Airflow files?: Atlas can invoke `ruff format` on any modified Apache Airflow file. It computes a unified diff for every edit and can apply `ruff format` to that diff, ensuring your `dags/` code adheres to styling standards before committing, preventing formatting regressions.
Can Atlas rewrite PythonOperators to use the TaskFlow API in Airflow?: Yes, Atlas can rewrite a chain of `PythonOperator` tasks within your Apache Airflow DAGs to leverage the TaskFlow API, transforming XCom passing into plain Python return values for cleaner, more idiomatic code, improving DAG readability and maintainability.
How does Atlas prevent accidental changes to my Apache Airflow codebase?: Atlas employs multiple safety measures: it drafts a plan in a read-only agent, every tool call is permission-gated, and it presents a unified diff for every file edit for your approval before writing any changes to your `dags/` or `pyproject.toml` files, ensuring control over your Apache Airflow project.

Try Atlas in your terminal

The terminal-native AI coding agent. Free core, single binary.

Install Atlas

Related guides

Refactor a Legacy Module with Atlas in 2026

How to refactor a legacy module with Atlas in 2026: findReferences maps every callsite, apply_patch refuses to apply against a drifted file, and bash proves behavior.

Trace a runtime bug from a stack trace in Apache Airflow with Atlas in 2026

Pinpoint and fix runtime bugs in Apache Airflow DAGs using Atlas, the terminal-native AI coding agent. Go from a production stack trace to a precise fix without a debugger attached.

Diagnose a Hanging or Long-Running Command in Apache Airflow with Atlas in 2026

In 2026, use Atlas to diagnose and resolve hanging or slow Apache Airflow commands. Identify interactive input blocks or genuinely slow scripts within your DAGs and unblock them efficiently.

Document an Apache Airflow Module with a README in 2026 using Atlas

For Apache Airflow developers in 2026, Atlas generates accurate READMEs for modules by analyzing live code, ensuring documentation reflects current behavior, not outdated plans. It integrates with `pytest (DagBag)` and

Extract a Shared Helper from Duplicated Code in Apache Airflow with Atlas in 2026

Streamline your Apache Airflow DAGs in 2026 by extracting duplicated logic into a shared, tested helper using Atlas. Find semantic duplicates, create new modules, and apply changes with confidence.

Migrate a Deprecated API Across Every Callsite in Apache Airflow with Atlas in 2026

Efficiently migrate deprecated APIs across your entire Apache Airflow codebase with Atlas. Enumerate all callsites, apply context-anchored patches, and validate changes with `pytest (DagBag)` and `ruff format` for a

Review a pull request in Apache Airflow with Atlas in 2026

Streamline Apache Airflow pull request reviews in 2026 with Atlas. Catch subtle bugs in DAG definitions, task dependencies, and top-level code using AI-powered context and real Airflow tools.

Locate Where a Behavior is Implemented in Apache Airflow with Atlas in 2026

Pinpoint the exact file and symbol responsible for any behavior in Apache Airflow using Atlas. Leverage semantic search, grep, and LSP tools for precise code navigation.

Browse this resource hub