Seokar is a powerful and comprehensive Python library designed for in-depth on-page SEO analysis of HTML content. It helps developers and SEO professionals to audit web pages, identify issues, and get actionable recommendations to improve search engine visibility and user experience.
The library is thread-safe, memory-efficient, and provides detailed insights into meta tags, content quality, page structure, links, social media presence, and structured data.
noindex, nofollow), Canonical URL (presence, correctness), Viewport (configuration for mobile), Charset (declaration, UTF-8 recommendation), HTML Language (declaration).nofollow, sponsored, ugc attributes, anchor text distribution (generic, branded, keyword-like).<base> tag.Literal for Enums) for enhanced code quality and IDE support, and dataclasses for structured results. Optimized for memory usage with __slots__.You can install Seokar using pip:
pip install seokar
(Note: Ensure the package name seokar is available on PyPI or use your chosen unique name).
Here's a simple example of how to use Seokar:
from seokar import Seokar, SEOResultLevel, __version__
# Sample HTML content (can be fetched from a URL using requests or other libraries)
html_document = """
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>My Awesome Test Page - SEO Analysis Example</title>
<meta name="description" content="A short but sweet description for this testing page. It aims to be optimal.">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="canonical" href="https://example.com/test-page">
<!-- <meta name="robots" content="noindex"> -->
</head>
<body>
<h1>Main Heading for the Page: SEO Rocks!</h1>
<p>This is some paragraph text. It's important for content quality analysis. We need enough content to pass the minimum length requirement and to check readability.</p>
<h2>A Subheading Here</h2>
<p>More content goes here. Links are also important.</p>
<img src="image.png" alt="A descriptive alt text for the image example">
<img src="decorative.gif" alt=""> <!-- Decorative image -->
<p>
An internal link: <a href="/internal-page">Click Here</a><br>
An external link: <a href="https://externalsite.com" rel="nofollow">External Site</a>
</p>
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "WebPage",
"name": "My Awesome Test Page"
}
</script>
</body>
</html>
"""
page_url = "https://example.com/test-page"
# Initialize the analyzer
analyzer = Seokar(html_content=html_document, url=page_url)
# Get the library version
print(f"Using Seokar version: {__version__}") # or analyzer.get_version()
# Perform the analysis
report = analyzer.analyze()
# Print the overall SEO Health Score
print(f"\nOverall SEO Health Score: {report['seo_health']['score']}%")
print(f"Critical Issues: {report['seo_health']['critical_issues_count']}")
print(f"Error Issues: {report['seo_health']['error_issues_count']}")
print(f"Warning Issues: {report['seo_health']['warning_issues_count']}")
# Print some basic SEO info
print(f"\nPage Title: {report['basic_seo']['title']}")
print(f"Meta Description: {report['basic_seo']['meta_description']}")
print(f"Canonical URL: {report['basic_seo']['canonical_url']}")
# Print issues with WARNING level or higher
print("\nIdentified Issues (Warning or higher):")
for issue in report['issues']:
# The issue['level'] is a dictionary representation of the SEOResultLevel enum
# Access 'value' for numeric comparison or 'name' for string representation
if issue['level']['value'] >= SEOResultLevel.WARNING.value:
print(f"- Type: {issue['element_type']}, Level: {issue['level']['name']}")
print(f" Message: {issue['message']}")
if issue['details']:
print(f" Details: {issue['details']}")
if issue['recommendation']:
print(f" Recommendation: {issue['recommendation']}")
# Example: Accessing keyword density
# print("\nTop Keywords (Content Quality):")
# for term, density in report['content_quality'].get('keyword_density_top_10_with_bigrams', {}).items():
# print(f"- '{term}': {density}%")
The analyze() method returns a dictionary containing a comprehensive breakdown of the SEO analysis. Key top-level keys in the report include:
analyzer_version: Version of Seokar used.url: The URL analyzed.basic_seo: Information on title, meta description, robots, canonical, viewport, charset, language, favicon.headings: Data on H1-H6 tags, including their content, count, and hierarchy assessment.content_quality: Analysis of content length, text-to-HTML ratio, readability score, and keyword density.images: Findings related to image alt texts.links: Details about internal and external links, nofollow attributes, and anchor text patterns.social_media_tags: Extracted Open Graph and Twitter Card tags.structured_data: Detected JSON-LD, Microdata, and RDFa, including Schema.org types.seo_health: The overall SEO score and counts of critical, error, and warning issues.issues: A list of all individual findings (dictionaries representing SEOResult objects, including severity level, message, details, and recommendation).recommendations: A consolidated list of unique, actionable recommendations for high-severity issues.Each sub-dictionary contains more granular data relevant to that specific SEO aspect.
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
git checkout -b feature/AmazingFeature).git commit -m 'Add some AmazingFeature').git push origin feature/AmazingFeature).Please make sure to update documentation or tests as appropriate and follow the existing code style. You can also simply open an issue with the tag "enhancement" or "bug". Don't forget to give the project a star! Thanks again!
Distributed under the MIT License. See LICENSE file for more information.
Sajjad Akbari - sajjadakbari.ir@email.com
Project Link: https://github.com/sajjadeakbari/seokar
FAQs
A Python library for comprehensive on-page SEO analysis of HTML content.
We found that seokar demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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