Better docstrings, better AI.
ruff checks how your docstrings look. interrogate checks if they exist. docvet checks if they're right. Existing tools cover presence and style — docvet delivers the layers they miss:
| Layer | Check | ruff | interrogate | pydoclint | docvet |
|---|---|---|---|---|---|
| 1. Presence | "Does a docstring exist?" | -- | Yes | -- | -- |
| 2. Style | "Is it formatted correctly?" | Yes | -- | -- | -- |
| 3. Completeness | "Does it have all required sections?" | -- | -- | Partial | Yes |
| 4. Accuracy | "Does it match the current code?" | -- | -- | -- | Yes |
| 5. Rendering | "Will mkdocs render it correctly?" | -- | -- | -- | Yes |
| 6. Visibility | "Will mkdocs even see the file?" | -- | -- | -- | Yes |
pydoclint covers 3 structural categories (Args, Returns, Raises). docvet's enrichment alone has 10 rules, including Raises, Yields, Receives, Warns, Attributes, Examples, and cross-references. Add freshness (git diff/blame staleness detection), griffe rendering compatibility, and mkdocs coverage: 19 rules across 4 checks, in territory no other tool touches.
Quickstart | GitHub Action | Pre-commit | Configuration | AI Agent Integration | Docs
Enrichment (completeness) -- 10 rules:
missing-raises missing-yields missing-receives missing-warns missing-other-parameters missing-attributes missing-typed-attributes missing-examples missing-cross-references prefer-fenced-code-blocks
Freshness (accuracy) -- 5 rules:
stale-signature stale-body stale-import stale-drift stale-age
Griffe (rendering) -- 3 rules:
griffe-unknown-param griffe-missing-type griffe-format-warning
Coverage (visibility) -- 1 rule:
missing-init
pip install docvet && docvet check --allFor optional griffe rendering checks:
pip install docvet[griffe]Example output:
src/mypackage/utils.py:42: missing-raises Function 'parse_config' raises ValueError but has no Raises section
src/mypackage/models.py:15: stale-signature Function 'process' signature changed but docstring not updated
src/mypackage/api.py:1: missing-init Package directory missing __init__.py (invisible to mkdocs)
Configure via [tool.docvet] in your pyproject.toml. All checks run and print findings. Checks listed in fail-on cause a non-zero exit code; unlisted checks are treated as warnings.
[tool.docvet]
exclude = ["tests", "scripts"]
fail-on = ["griffe", "coverage"]
[tool.docvet.freshness]
drift-threshold = 30
age-threshold = 90Add to your .pre-commit-config.yaml:
repos:
- repo: https://github.com/Alberto-Codes/docvet
rev: v1.2.0
hooks:
- id: docvetFor griffe rendering checks, add the optional dependency:
repos:
- repo: https://github.com/Alberto-Codes/docvet
rev: v1.2.0
hooks:
- id: docvet
additional_dependencies: [griffe]Add docvet to your GitHub Actions workflow:
- uses: Alberto-Codes/docvet@v1With version pinning and custom arguments:
- uses: Alberto-Codes/docvet@v1
with:
version: '1.2.0'
args: 'check --all'For griffe rendering checks, install griffe before running docvet:
- uses: actions/setup-python@v5
with:
python-version: '3.12'
- run: pip install griffe
- uses: Alberto-Codes/docvet@v1
with:
args: 'check --all'For tool-specific integration snippets, see the full AI Agent Integration guide.
Add docvet to your AI coding workflow. Drop this into your CLAUDE.md, .cursorrules, or agent configuration:
## Docstring Quality
After modifying Python functions, classes, or modules, run `docvet check` and fix all findings before committing.Recommended pyproject.toml configuration:
[tool.docvet]
fail-on = ["enrichment", "freshness", "coverage", "griffe"]| Command | Description |
|---|---|
docvet check |
Run all enabled checks (default: git diff files) |
docvet check --all |
Run all checks on entire codebase |
docvet check --staged |
Run all checks on staged files only |
docvet enrichment |
Check for missing docstring sections |
docvet freshness |
Detect stale docstrings via git |
docvet coverage |
Find files invisible to mkdocs |
docvet griffe |
Check mkdocs rendering compatibility |
AI coding agents rely on docstrings as context when generating and modifying code. Agents modify code but often leave docstrings stale, and research shows stale or incorrect documentation is actively harmful, worse than no docs at all:
- Incorrect docs degrade LLM task success by 22.6 percentage points
- Comment density improves code generation by 40-54%
- Misleading comments reduce LLM fault localization accuracy to 24.55%
- Performance drops substantially without docstrings
As the 2025 DORA report puts it: "AI doesn't fix a team; it amplifies what's already there." The only signal correlating with AI productivity is code quality.
docvet's freshness checking catches the accuracy gap that stale docs create, and its enrichment rules ensure the docstring sections that agents use as context are complete. Run docvet check in your CI, pre-commit hooks, or agent toolchain.
Add a badge to your project to show your docs are vetted:
[](https://github.com/Alberto-Codes/docvet)Are you using docvet? Open a pull request to add your project here.
MIT -- see LICENSE for details.