How does Atlas ensure it finds all calls to a deprecated Flask function?

Atlas combines `lsp`'s `findReferences` for AST-based accuracy with `grep` for dynamic or string-based usages, ensuring 100% coverage across your Flask project, including within blueprints and application factories.

What if my Flask codebase has drifted since the patch was generated?

Atlas's `apply_patch` tool is designed to fail explicitly if the expected context or old lines are not found, preventing misapplication to a drifted Flask file. This ensures changes are only applied precisely.

Can Atlas run my Flask tests after each change?

Yes, Atlas integrates with your Flask testing setup. It uses the `bash` tool to execute `pytest (app.test_client)` after each file modification, ensuring immediate verification of functional correctness.

How does Atlas handle code formatting in Flask after a migration?

Atlas can automatically run `ruff format` on touched Flask blueprints and other files, ensuring your codebase remains consistent with your project's formatting standards after the migration.

Is Atlas suitable for large Flask applications with many blueprints?

Absolutely. Atlas's ability to index code by AST declarations using tree-sitter and fan out work to subagents makes it highly effective for large Flask applications, managing migrations across numerous blueprints and modules efficiently.

How does Atlas ensure I review changes before they are written to my Flask project?

Atlas computes a unified diff for every file edit and surfaces it for your approval before writing. It also drafts a plan in a read-only agent and asks for permission before switching to a build agent, giving you full control over changes to your Flask codebase.

Can Atlas help move module-level config in Flask?

Yes, as part of its setup, Atlas can be asked to move module-level config out of the import path and into the application factory, aligning with best practices for testable Flask applications.

Migrate a Deprecated API Across Every Callsite in Flask with Atlas in 2026

Atlas helps Flask developers in 2026 migrate deprecated APIs across an entire codebase by enumerating every callsite, tracking progress with `todowrite`, and applying context-anchored patches that integrate with `pytest (app.test_client)` and `ruff format` for robust, verified changes.

How to find all deprecated API calls in a Flask project?

Atlas uses the `lsp` tool's `findReferences` operation to precisely locate every callsite of a deprecated Flask API, ensuring no usage is missed in your 2026 codebase. This method is far more reliable than simple string searches, especially for complex Flask patterns like blueprints, covering 100% of direct calls.

To begin migrating a deprecated API in Flask, the first critical step is to identify every single place it is called. Atlas achieves this by leveraging its `lsp` tool, which interfaces with your language server to perform a `findReferences` operation on the deprecated symbol. This provides a complete and accurate list of all direct callers, understanding the code's Abstract Syntax Tree (AST) rather than just text. For Flask applications, this is crucial for correctly identifying usages within blueprints, application factories, or even extensions initialized in `create_app()`. Atlas then cross-checks these results with `grep` for any dynamic or string-based usages that might evade `lsp`, such as a function name passed as a string to a utility, ensuring a truly exhaustive enumeration across your Flask project.

How to track migration progress for Flask API deprecations?

To manage the migration of a deprecated Flask API, Atlas creates one `todowrite` entry for each identified callsite, providing a clear, visible record of partial progress across your project in 2026. This prevents silent skips and ensures accountability for all changes, even across 100s of files.

Migrating a deprecated API across an entire Flask codebase can be a substantial task, often spanning many files and requiring multiple commits. Atlas addresses this challenge by integrating the `todowrite` tool into the workflow. After enumerating all callsites, Atlas generates a distinct `todowrite` entry for each one. This creates a granular, trackable list of work items. As each callsite in a Flask blueprint or module is migrated, its corresponding `todowrite` entry is updated, providing a transparent view of progress. This approach ensures that no callsite is silently skipped or forgotten, making partial progress visible and manageable, especially in large Flask applications where many developers might contribute.

How to safely apply API migration patches in Flask?

Atlas migrates each deprecated Flask API callsite using `apply_patch`, a tool designed to seek the hunk's context and old lines, preventing misapplication to drifted files in your 2026 project. This ensures that every change is precise and context-aware, crucial for Flask's request context and application factory patterns, with a 0% chance of misapplication.

Applying changes across a large Flask codebase requires precision and safety. Atlas employs the `apply_patch` tool for this purpose. Instead of blindly applying changes, `apply_patch` is designed to seek out the exact hunk's context and the original `old_lines` before making any modification. If the expected lines or context are not found,perhaps due to a file drifting since the initial enumeration,`apply_patch` will explicitly throw a 'Failed to find expected lines' error rather than guessing or misapplying the patch. This robust mechanism is vital for Flask applications, where changes might affect sensitive areas like the application factory (`create_app()`), blueprints, or components interacting with `current_app` or `g`, ensuring that every migration is applied with surgical accuracy and without unintended side effects.

How to verify Flask API migrations with tests and formatting?

After migrating each Flask API callsite, Atlas runs affected tests using `pytest (app.test_client)` via the `bash` tool, marking the `todowrite` entry complete only upon successful execution in 2026. This rigorous 1-step verification ensures functional correctness and maintains code quality, integrating directly with your `pyproject.toml` setup.

Verification is paramount in any API migration, especially in Flask where changes can impact request handling and application state. Atlas integrates directly with your Flask testing workflow. After `apply_patch` modifies a file, Atlas uses the `bash` tool to execute `pytest (app.test_client)` for the affected tests. This immediate feedback loop ensures that the migration has not introduced regressions. Only when `pytest (app.test_client)` passes successfully is the corresponding `todowrite` entry marked as complete. Furthermore, Atlas can be configured to run `ruff format` on any touched Flask blueprints or modules, ensuring that code style remains consistent throughout the migration process, adhering to your `pyproject.toml` configuration and maintaining high code quality standards.

How to finalize a deprecated Flask API migration?

To finalize a deprecated Flask API migration, Atlas performs a final `grep` for the old symbol, confirming zero remaining hits across your 22,000-line codebase in 2026, before safely deleting the old implementation. This ensures a clean, complete transition, leaving no trace of the deprecated code.

The final stage of migrating a deprecated Flask API is to ensure its complete removal and cleanup. Once all `todowrite` entries are marked complete and all tests pass, Atlas performs a conclusive `grep` for the deprecated symbol across the entire Flask project. This final check confirms that absolutely zero instances of the old API remain, including any dynamic or string-based references that might have been missed. Only after this confirmation does Atlas proceed to delete the old implementation of the deprecated function or module. This meticulous approach guarantees a clean and thorough migration, preventing future developers from inadvertently reintroducing or encountering the deprecated API in your Flask application.

Step by step

01Run Atlas in your Flask project, ensuring a `pyproject.toml` and an `app` package exposing `create_app()` are present.
02Use `atlas lsp findReferences <deprecated_symbol>` to enumerate all callsites of the deprecated Flask API.
03Cross-check the `lsp` results with `atlas grep "<deprecated_symbol_string>"` for dynamic or string-based usages within your Flask blueprints and modules.
04Create one `todowrite` entry for each identified callsite: `atlas todowrite add "Migrate <file_path>:<line_number>"`.
05For each `todowrite` entry, use `atlas apply_patch` to migrate the callsite, ensuring it seeks the hunk's context and old lines.
06After each `apply_patch` operation, run `atlas bash "pytest (app.test_client) <affected_test_file>"` to verify functional correctness.
07Mark the `todowrite` entry complete only after `pytest (app.test_client)` passes successfully.
08Once all `todowrite` entries are complete, run `atlas grep "<deprecated_symbol>"` to confirm zero remaining hits across your Flask codebase.
09Delete the old implementation of the deprecated Flask API, ensuring a clean removal.
10Let Atlas run `ruff format` on all touched Flask blueprints and files, then review the diff and stage the changes.

Frequently asked questions

How does Atlas ensure it finds all calls to a deprecated Flask function?: Atlas combines `lsp`'s `findReferences` for AST-based accuracy with `grep` for dynamic or string-based usages, ensuring 100% coverage across your Flask project, including within blueprints and application factories.
What if my Flask codebase has drifted since the patch was generated?: Atlas's `apply_patch` tool is designed to fail explicitly if the expected context or old lines are not found, preventing misapplication to a drifted Flask file. This ensures changes are only applied precisely.
Can Atlas run my Flask tests after each change?: Yes, Atlas integrates with your Flask testing setup. It uses the `bash` tool to execute `pytest (app.test_client)` after each file modification, ensuring immediate verification of functional correctness.
How does Atlas handle code formatting in Flask after a migration?: Atlas can automatically run `ruff format` on touched Flask blueprints and other files, ensuring your codebase remains consistent with your project's formatting standards after the migration.
Is Atlas suitable for large Flask applications with many blueprints?: Absolutely. Atlas's ability to index code by AST declarations using tree-sitter and fan out work to subagents makes it highly effective for large Flask applications, managing migrations across numerous blueprints and modules efficiently.
How does Atlas ensure I review changes before they are written to my Flask project?: Atlas computes a unified diff for every file edit and surfaces it for your approval before writing. It also drafts a plan in a read-only agent and asks for permission before switching to a build agent, giving you full control over changes to your Flask codebase.
Can Atlas help move module-level config in Flask?: Yes, as part of its setup, Atlas can be asked to move module-level config out of the import path and into the application factory, aligning with best practices for testable Flask applications.

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