How does Atlas ensure FastAPI documentation is always up-to-date?

Atlas ensures FastAPI documentation is always current by reading the live source code using `lsp` and `read` tools, rather than relying on outdated comments or memory. It then verifies code samples with `bash` and `pytest (httpx AsyncClient)`.

Can Atlas document FastAPI `Pydantic` models and `Depends()` functions?

Yes, Atlas specifically understands FastAPI's `Pydantic v2` models and `Depends()` dependency injection. It uses `lsp` to enumerate them and `read` and `codebase_search` to document their behavior and usage within your application.

What FastAPI testing tools does Atlas integrate with for documentation verification?

Atlas integrates directly with `pytest (httpx AsyncClient)` for verifying FastAPI code samples. It can generate and run these tests to confirm the accuracy and functionality of documented API interactions.

How does Atlas handle existing `README.md` files in a FastAPI project?

Atlas uses the `grep` tool to analyze existing `README.md` files in your FastAPI project. This allows it to match the established heading structure, tone, and formatting, ensuring new documentation is consistent with your project's style.

Is it safe to let Atlas modify my FastAPI codebase for documentation?

Yes, Atlas is designed with safety in mind. All tool calls are permission-gated, and every proposed file edit, including `README.md` changes, is presented as a unified diff for your explicit approval before being written to disk. Atlas also snapshots changes as git patches for easy rollback.

Can Atlas help improve my FastAPI code quality while documenting?

While its primary role is documentation, Atlas can be instructed to add `response_model` to FastAPI endpoints to prevent data leakage. It can also run `ruff format` and `ruff check --fix` on any new code it generates or modifies, aligning with your project's code style.

Does Atlas support FastAPI projects using `uv` for package management?

Yes, Atlas is compatible with FastAPI projects using `uv` as their package manager. It operates within your project's environment, respecting your `pyproject.toml` and existing toolchain configurations.

Document a FastAPI Module with a README in 2026

Atlas, the terminal-native AI coding agent, helps FastAPI developers produce documentation that accurately describes what their code does today, not what it was supposed to do a year ago, by leveraging real-time code analysis with tools like `lsp`, `read`, and `codebase_search`, and verifying outputs with `bash` and `pytest (httpx AsyncClient)`.

How Atlas enumerates FastAPI module APIs for documentation

Atlas enumerates a FastAPI module's public API by employing the `lsp` tool's `documentSymbol` operation, ensuring a 100% complete and accurate list of exported symbols. This method guarantees that every Pydantic model, APIRouter include, and path operation in your `main.py` or other module is identified for documentation.

To create documentation that reflects the current state of your FastAPI application, Atlas begins by using the `lsp` tool. This tool's `documentSymbol` operation provides a precise, machine-readable list of all public API elements within a given module. For a FastAPI project, this means Atlas identifies every `APIRouter` inclusion, every `Pydantic v2` model definition, and every path operation decorated with `@app.get()`, `@app.post()`, etc., along with their `Depends()` injections. This approach prevents omissions or inventions, ensuring the generated README accurately covers the module's entire public surface as it exists in 2026, not based on outdated assumptions or comments.

How Atlas reads FastAPI implementation and usage for accurate docs

Atlas reads the implementation of each FastAPI export and uses `codebase_search` to understand how callers actually use it in practice, providing a 360-degree view of its behavior. This ensures the documentation reflects real-world usage patterns, including how `Pydantic` models are consumed and `Depends()` functions are utilized across your project.

After enumerating the public API, Atlas delves into the actual implementation of each FastAPI component. It uses the `read` tool to inspect the source code of path operations, `Pydantic` models, and dependency functions. Crucially, Atlas then employs `codebase_search` to find concrete examples of how these components are invoked and integrated throughout the codebase. For instance, it can identify how a specific `Pydantic` model is used as a `response_model` or a request body, or how a `Depends()` function is injected into multiple path operations. This dual approach of reading definitions and observing practical usage ensures the documentation describes the code's actual behavior and common idioms, rather than just its theoretical design, making it highly relevant for a FastAPI developer in 2026.

Generating a FastAPI README with Atlas's write tool

Atlas generates the FastAPI module's README using its `write` tool, after first using `grep` to find an existing `README.md` to match its heading structure and tone. This ensures the new documentation integrates direct into your project's existing style, providing a consistent experience for developers in 2026.

When it's time to write the documentation, Atlas prioritizes consistency. Before generating new content, it uses the `grep` tool to search the repository for an existing `README.md` file. This allows Atlas to analyze the established heading structure, tone, and formatting conventions. By adhering to these existing patterns, Atlas ensures the new module README aligns perfectly with your project's documentation style, avoiding a disjointed appearance. The `write` tool then composes the README, incorporating real signatures from your FastAPI code, actual file paths, and usage examples derived from its `read` and `codebase_search` analysis. This process results in a README that is not only accurate but also stylistically consistent with your project's existing documentation.

Verifying FastAPI code samples in documentation with bash and pytest

Atlas verifies every code sample included in the FastAPI module's documentation by executing it with `bash`, and for more complex scenarios, running `pytest (httpx AsyncClient)` behind a permission prompt. This rigorous verification process ensures that 100% of the code examples are functional and accurate, preventing the common issue of outdated or broken snippets.

A key differentiator for Atlas is its commitment to verifiable documentation. Every code sample generated for the FastAPI module's README is not merely plausible; it is executed. Atlas uses the `bash` tool to run simple command-line examples directly. For FastAPI-specific code, such as API calls or integration tests, Atlas can generate and then execute `pytest` tests using `httpx AsyncClient`. This execution happens behind a permission prompt, giving you control. If a sample fails, Atlas iterates on the documentation or the sample itself until it passes. This ensures that any `uv` install command, `ruff format` invocation, or `httpx AsyncClient` request shown in the README is guaranteed to work as described, providing reliable guidance for developers in 2026.

Atlas's safety and review mechanisms for FastAPI documentation

Atlas incorporates robust safety and review mechanisms throughout the FastAPI documentation workflow, including permission-gated tool calls and unified diffs for every file edit. Before any changes are written, Atlas drafts a plan in a read-only agent and asks for approval, ensuring you maintain full control over your codebase in 2026.

Atlas is designed with developer control and safety as core principles. Every tool call, whether it's `lsp`, `read`, `write`, or `bash`, is permission-gated against `allow`, `ask`, and `deny` rules. Before making any modifications, Atlas drafts a comprehensive plan in a read-only plan agent and explicitly asks for your approval. Once approved, it switches to a build agent. For every file edit, including the creation or modification of a `README.md`, Atlas computes a unified diff and surfaces it for your review and approval before writing. This transparent process, combined with Atlas's ability to snapshot file changes as git patches for easy rollback, ensures that all documentation changes for your FastAPI project are intentional, reviewed, and fully reversible, providing peace of mind for developers in 2026.

Step by step

01Run Atlas in your FastAPI project directory, ensuring a `pyproject.toml` and a `main.py` instantiating `FastAPI()` are present.
02Allow Atlas to read your `Pydantic v2` models, `APIRouter` includes, and every `Depends()` in your path operations using the `lsp` and `read` tools.
03Instruct Atlas to use `grep` to find an existing `README.md` to match its structure and tone, then `write` the new module documentation.
04Approve Atlas's plan to `write` the `README.md`, reviewing the proposed content and structure.
05Let Atlas verify every code sample in the generated `README.md` by running it with `bash` and `pytest (httpx AsyncClient)` behind a permission prompt.
06Review the unified diff for the `README.md` and approve the changes, ensuring the documentation accurately reflects your FastAPI code.
07Optionally, let Atlas run `ruff format` and `ruff check --fix` on any new code samples or files it might have generated during verification.
08Approve the final diff for any formatting changes, then let Atlas stage and create a commit for your updated FastAPI documentation.

Frequently asked questions

How does Atlas ensure FastAPI documentation is always up-to-date?: Atlas ensures FastAPI documentation is always current by reading the live source code using `lsp` and `read` tools, rather than relying on outdated comments or memory. It then verifies code samples with `bash` and `pytest (httpx AsyncClient)`.
Can Atlas document FastAPI `Pydantic` models and `Depends()` functions?: Yes, Atlas specifically understands FastAPI's `Pydantic v2` models and `Depends()` dependency injection. It uses `lsp` to enumerate them and `read` and `codebase_search` to document their behavior and usage within your application.
What FastAPI testing tools does Atlas integrate with for documentation verification?: Atlas integrates directly with `pytest (httpx AsyncClient)` for verifying FastAPI code samples. It can generate and run these tests to confirm the accuracy and functionality of documented API interactions.
How does Atlas handle existing `README.md` files in a FastAPI project?: Atlas uses the `grep` tool to analyze existing `README.md` files in your FastAPI project. This allows it to match the established heading structure, tone, and formatting, ensuring new documentation is consistent with your project's style.
Is it safe to let Atlas modify my FastAPI codebase for documentation?: Yes, Atlas is designed with safety in mind. All tool calls are permission-gated, and every proposed file edit, including `README.md` changes, is presented as a unified diff for your explicit approval before being written to disk. Atlas also snapshots changes as git patches for easy rollback.
Can Atlas help improve my FastAPI code quality while documenting?: While its primary role is documentation, Atlas can be instructed to add `response_model` to FastAPI endpoints to prevent data leakage. It can also run `ruff format` and `ruff check --fix` on any new code it generates or modifies, aligning with your project's code style.
Does Atlas support FastAPI projects using `uv` for package management?: Yes, Atlas is compatible with FastAPI projects using `uv` as their package manager. It operates within your project's environment, respecting your `pyproject.toml` and existing toolchain configurations.

Try Atlas in your terminal

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

Install Atlas

Related guides

Document a Module with a README Using Atlas (2026 Workflow)

How to document a module with a README using Atlas in 2026: the lsp tool's documentSymbol enumerates the real exports, read supplies the behavior, write emits the README.

Atlas for FastAPI in 2026

Atlas is a terminal-native AI coding agent for FastAPI in 2026. It reads Pydantic v2 models and Depends(), then runs pytest behind a permission prompt.

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

Diagnose hanging or slow FastAPI commands with Atlas in 2026. Identify blocked `uv` builds or `pytest` runs, using Atlas's bash tool to get unstuck and optimize your FastAPI development workflow.

Add a Regression Test for a Bug Fix in FastAPI with Atlas in 2026

Lock in FastAPI bug fixes with Atlas in 2026. Learn to write regression tests that fail before the fix and pass after, using `pytest (httpx AsyncClient)` and `uv` for robust development.

Locate where a behavior is implemented in FastAPI with Atlas in 2026

Discover how Atlas helps FastAPI developers in 2026 pinpoint the exact file and symbol for any behavior. Use semantic search, grep, and LSP tools to navigate your codebase efficiently and safely.

Trace a runtime bug from a stack trace in FastAPI with Atlas in 2026

Pinpoint and fix FastAPI runtime bugs from production stack traces using Atlas, the terminal-native AI coding agent. Leverage real FastAPI tools like pytest (httpx AsyncClient) and uv.

Review a pull request in FastAPI with Atlas in 2026

In 2026, use Atlas to review FastAPI pull requests. Go beyond line-by-line diffs by leveraging `pytest (httpx AsyncClient)`, `uv`, and `ruff format` for comprehensive code quality and bug detection.

Research a third-party API before integrating it in FastAPI with Atlas in 2026

Streamline third-party API research for FastAPI integrations in 2026 using Atlas. Get current API shapes, generate Pydantic models, and write httpx AsyncClient tests with AI assistance.

Browse this resource hub