How does Atlas identify which FastAPI endpoints need tests?

Atlas uses its `read` tool to parse your FastAPI project's source code and then employs the `lsp` tool's `documentSymbol` operation. This precisely enumerates all public FastAPI path operations and functions, allowing Atlas to target those without existing `pytest (httpx AsyncClient)` coverage.

Can Atlas use my existing `pytest` setup and fixtures for FastAPI testing?

Yes, Atlas is designed to adapt. It uses the `grep` tool to scan your repository for existing `test_*.py` files, learning your current `pytest` framework, `httpx AsyncClient` usage, import styles, and naming conventions. It then generates new tests that direct integrate with your established FastAPI testing environment.

What FastAPI testing tools does Atlas support?

Atlas primarily supports `pytest` as the test runner, specifically leveraging `httpx.AsyncClient` for making requests to your FastAPI application. It also integrates with `uv` for package management and `ruff format` and `ruff check --fix` for code formatting and linting, aligning with modern FastAPI development practices in 2026.

How does Atlas ensure the quality and style of generated FastAPI tests?

After generating and passing tests, Atlas automatically runs `ruff format` to apply your project's formatting rules and `ruff check --fix` to address linting issues. This ensures that all new `pytest (httpx AsyncClient)` tests adhere to your team's code quality and style standards, making them maintainable and consistent with your existing FastAPI codebase.

Is it safe to let Atlas modify my FastAPI codebase?

Yes, Atlas is built with safety and transparency in mind. Every proposed change, whether it's new `pytest` tests or modifications to your FastAPI application, is presented as a unified diff for your explicit approval. Atlas also snapshots file changes as `git` patches, allowing for easy rollback, ensuring you always have control over your codebase.

Can Atlas handle FastAPI projects with complex Pydantic models and `Depends()` injections?

Yes, Atlas is designed to understand complex FastAPI structures. It reads your Pydantic v2 models, `APIRouter` includes, and every `Depends()` in your path operations. This deep understanding allows it to generate accurate `httpx AsyncClient` tests that correctly interact with your FastAPI application's dependency injection and data validation.

What if my FastAPI test suite generates a very large output?

Atlas handles large test outputs efficiently. If the `pytest` output exceeds 2000 lines or 50 KB, Atlas truncates the terminal display for readability but saves the complete log to a file. This ensures you can always access the full details of your FastAPI test run for thorough debugging and analysis.

Write Unit Tests for Untested FastAPI Code with Atlas in 2026

Atlas empowers FastAPI developers in 2026 to efficiently write unit tests for untested code, leveraging your existing `pytest (httpx AsyncClient)` conventions. It integrates direct with your FastAPI project, using `uv` for package management and `ruff format` for consistent code styling, ensuring your new tests are production-ready and maintainable.

How Atlas Identifies Untested FastAPI Endpoints and Functions

Atlas identifies untested FastAPI endpoints and functions by first reading your project's source code, then using its `lsp` tool to enumerate all exported symbols. This process ensures that no public path operation or utility function is missed, providing a comprehensive list for test coverage in 2026.

To begin writing unit tests for your FastAPI application, Atlas first needs to understand its structure. Atlas uses its `read` tool to ingest your project's Python files, including `main.py` and any modules containing `APIRouter` definitions. Following this, the `lsp` tool's `documentSymbol` operation is invoked. This operation, powered by tree-sitter for AST declarations, precisely lists every public function, class, and FastAPI path operation (like `@app.get("/items/")` or `@router.post("/users/")`) within your codebase. This detailed symbol enumeration ensures that Atlas has a complete inventory of all testable units, allowing it to systematically address areas lacking `pytest (httpx AsyncClient)` coverage. The agent drafts a plan in a read-only plan agent, ensuring all identified symbols are considered before any code generation begins.

Adopting Existing FastAPI Test Conventions with Atlas

Atlas ensures new FastAPI unit tests adhere to your repository's established conventions by using the `grep` tool to locate existing test files. This step is crucial for maintaining consistency across your codebase, ensuring that new `pytest (httpx AsyncClient)` tests match the style, import patterns, and naming conventions already in place in 2026.

Maintaining a consistent testing style is vital for long-term project health. Before generating any new test code, Atlas employs its `grep` tool to search for existing test files within your FastAPI project, typically files named `test_*.py`. This search helps Atlas identify your repository's specific `pytest (httpx AsyncClient)` framework setup, common import styles (e.g., relative vs. absolute imports), and naming conventions for test functions and classes. For instance, if your existing tests use `httpx.AsyncClient` for making requests to your FastAPI application and follow a `test_my_feature.py` naming pattern, Atlas will replicate these idioms. This approach prevents the introduction of inconsistent testing patterns, making the new tests feel native to your project and easier for other FastAPI developers to understand and maintain. Atlas's ability to copy these conventions means less time spent on manual style adjustments and more focus on test logic.

Generating and Running FastAPI Tests with `pytest (httpx AsyncClient)`

Atlas generates new FastAPI unit tests using its `write` tool, creating a new `test_*.py` file that leverages `httpx AsyncClient` for integration. After drafting, Atlas immediately runs the test suite with the `bash` tool, executing `pytest` to validate the new code. This ensures that every test written is a real, executable test, not just a theoretical one, providing immediate feedback in 2026.

Once Atlas has identified untested FastAPI code and understood your project's testing conventions, it proceeds to generate the actual test files. Using the `write` tool, Atlas drafts a new `test_your_module.py` file, populating it with `pytest` test functions designed to exercise the previously identified untested symbols. These tests will typically instantiate your FastAPI application and use `httpx.AsyncClient` to simulate HTTP requests to your path operations, verifying responses against expected Pydantic models or status codes. Before writing anything to disk, Atlas presents a unified diff for your approval, ensuring transparency and control. After approval, Atlas uses the `bash` tool to execute the newly generated tests alongside your existing suite by running the command `pytest`. This immediate execution is critical; a test that is never run provides no value. If the output exceeds 2000 lines or 50 KB, Atlas truncates it in the terminal but saves the full log to a file for detailed review, allowing you to quickly identify any failures.

Iterating and Refining FastAPI Tests with Atlas's Edit and Formatting Tools

Atlas facilitates the iteration and refinement of FastAPI unit tests by allowing you to `edit` failing tests directly within the terminal, then re-running `pytest` for rapid feedback. Once tests are green, Atlas automatically applies `ruff format` and `ruff check --fix` to ensure code quality, maintaining a consistent style across your project in 2026.

Achieving a fully passing test suite often requires several iterations. When `pytest` reports failures, Atlas allows you to use its `edit` tool to modify the generated test code or even the application code under test. This interactive feedback loop is crucial for debugging and refining tests until they pass. For large modules, Atlas can maintain a `todowrite` list, helping you track progress and focus on specific areas. After the tests are green and you've approved the changes, Atlas takes an additional step to ensure code quality and consistency. It automatically runs `ruff format` to apply your project's formatting rules and `ruff check --fix` to address any linting issues. This integration with standard FastAPI development tools like `ruff` ensures that the newly added tests not only function correctly but also adhere to your team's coding standards, making them easier to review and integrate into your `git` repository. Atlas also reads `git` branches, status, and diffs, and can stage and create commits on your behalf, streamlining the entire development workflow.

Ensuring Safety and Review for FastAPI Code Changes with Atlas

Atlas prioritizes safety and transparency when modifying your FastAPI codebase, presenting every proposed change as a unified diff for explicit approval before writing to disk. This permission-gated approach, combined with its ability to snapshot file changes as `git` patches, provides a robust review mechanism for all generated `pytest (httpx AsyncClient)` tests in 2026.

Security and control are paramount when an AI agent modifies your codebase. Atlas is designed with multiple layers of safety. Every tool call, including `write` and `bash`, is permission-gated against allow, ask, and deny rules, ensuring you retain full control. Before any generated `pytest (httpx AsyncClient)` test code or application code modifications are written to your FastAPI project, Atlas computes and surfaces a unified diff. This diff clearly shows exactly what changes Atlas proposes to make, allowing you to review and approve or reject them. Furthermore, Atlas snapshots file changes as `git` patches, providing a robust rollback mechanism. This means you can easily revert any changes if they don't meet your expectations. This transparent, approval-based workflow ensures that Atlas acts as a powerful assistant, not an autonomous agent, always requiring your explicit consent before altering your valuable FastAPI codebase, including `pyproject.toml` or `main.py`.

Step by step

01Initialize Atlas in your FastAPI project directory, ensuring a `pyproject.toml` and `main.py` are present.
02Ask Atlas to read your FastAPI module and use the `lsp` tool's `documentSymbol` operation to enumerate all public path operations and functions that require `pytest (httpx AsyncClient)` tests.
03Instruct Atlas to `grep` for an existing `test_*.py` file in your repository to identify and copy your project's specific `pytest` framework, `httpx AsyncClient` usage, import style, and naming conventions.
04Use the Atlas `write` tool to draft the new `test_your_module.py` spec file, which will include `httpx AsyncClient` calls for your FastAPI endpoints. Review and approve the presented diff.
05Execute the newly generated `pytest` suite using the Atlas `bash` tool with the command `pytest`. Review any failures, noting that output over 2000 lines or 50 KB is saved to a file.
06Iterate on failing tests by using the Atlas `edit` tool to refine the test code or the FastAPI application code. Re-run `pytest` until all tests pass, using `todowrite` for large modules.
07Approve the final diffs for the passing tests, then let Atlas run `ruff format` and `ruff check --fix` to ensure your new FastAPI tests adhere to code style and quality standards.
08Allow Atlas to stage and create a `git` commit for the new, passing, and formatted FastAPI unit tests.

Frequently asked questions

How does Atlas identify which FastAPI endpoints need tests?: Atlas uses its `read` tool to parse your FastAPI project's source code and then employs the `lsp` tool's `documentSymbol` operation. This precisely enumerates all public FastAPI path operations and functions, allowing Atlas to target those without existing `pytest (httpx AsyncClient)` coverage.
Can Atlas use my existing `pytest` setup and fixtures for FastAPI testing?: Yes, Atlas is designed to adapt. It uses the `grep` tool to scan your repository for existing `test_*.py` files, learning your current `pytest` framework, `httpx AsyncClient` usage, import styles, and naming conventions. It then generates new tests that direct integrate with your established FastAPI testing environment.
What FastAPI testing tools does Atlas support?: Atlas primarily supports `pytest` as the test runner, specifically leveraging `httpx.AsyncClient` for making requests to your FastAPI application. It also integrates with `uv` for package management and `ruff format` and `ruff check --fix` for code formatting and linting, aligning with modern FastAPI development practices in 2026.
How does Atlas ensure the quality and style of generated FastAPI tests?: After generating and passing tests, Atlas automatically runs `ruff format` to apply your project's formatting rules and `ruff check --fix` to address linting issues. This ensures that all new `pytest (httpx AsyncClient)` tests adhere to your team's code quality and style standards, making them maintainable and consistent with your existing FastAPI codebase.
Is it safe to let Atlas modify my FastAPI codebase?: Yes, Atlas is built with safety and transparency in mind. Every proposed change, whether it's new `pytest` tests or modifications to your FastAPI application, is presented as a unified diff for your explicit approval. Atlas also snapshots file changes as `git` patches, allowing for easy rollback, ensuring you always have control over your codebase.
Can Atlas handle FastAPI projects with complex Pydantic models and `Depends()` injections?: Yes, Atlas is designed to understand complex FastAPI structures. It reads your Pydantic v2 models, `APIRouter` includes, and every `Depends()` in your path operations. This deep understanding allows it to generate accurate `httpx AsyncClient` tests that correctly interact with your FastAPI application's dependency injection and data validation.
What if my FastAPI test suite generates a very large output?: Atlas handles large test outputs efficiently. If the `pytest` output exceeds 2000 lines or 50 KB, Atlas truncates the terminal display for readability but saves the complete log to a file. This ensures you can always access the full details of your FastAPI test run for thorough debugging and analysis.

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