This Langflow flow implements a comprehensive technical SEO auditing system that analyzes raw HTML and generates actionable fixes.

The workflow begins by accepting a webpage URL or raw HTML content through chat input, webhook triggers, or file uploads. URL components fetch the webpage content, retrieving the complete HTML source code including all metadata, structure elements, and embedded scripts. For direct HTML analysis, the system can process uploaded HTML files or receive HTML content through API calls.

HTML parsing components extract and structure the raw HTML code, identifying key elements including meta tags, title tags, heading hierarchies, schema markup, alt attributes, canonical links, and structural elements. Data Operations components normalize the HTML structure and extract specific SEO-relevant elements for detailed analysis.

An AI agent powered by OpenAI's language models performs strict technical SEO analysis on the parsed HTML. The agent receives detailed instructions through Prompt Template components that define SEO best practices, common error patterns, and validation criteria. The system checks for critical issues including missing or duplicate meta descriptions, improper heading structure (H1-H6 hierarchy), missing alt text on images, broken schema markup, missing canonical tags, improper Open Graph tags, and accessibility violations.

The analysis identifies specific errors with evidence-based diagnoses, pinpointing exact locations in the HTML where issues occur. The system categorizes issues by severity (critical, warning, informational) and provides detailed explanations of how each issue impacts SEO performance and search engine visibility.

Code generation components create ready-to-use fixes for identified issues. The system generates corrected HTML snippets, complete meta tag implementations, proper schema markup, and structural improvements that can be directly implemented. Each fix includes before-and-after code examples, making it easy for developers to understand and apply the corrections.

Structured Output components format the audit results into comprehensive reports with sections for identified errors, evidence citations, severity ratings, impact explanations, and ready-to-use code fixes. The reports can be delivered in multiple formats including JSON for API integration, Markdown for documentation, or HTML for visual presentation.

Vector store components can index historical audit results and successful fix patterns, enabling the system to learn from past audits and suggest improvements based on similar issues found in other websites. The system maintains a knowledge base of common SEO problems and their solutions for faster diagnosis and more accurate recommendations.

The flow can be extended using additional Langflow components to enhance SEO auditing capabilities. You can integrate web crawler components to audit entire websites by following links and analyzing multiple pages in sequence. API Request nodes can connect to SEO tools like Google Search Console or SEMrush to cross-reference audit findings with actual search performance data. Vector store bundles enable long-term storage of audit results and fix patterns for trend analysis and continuous improvement. Batch processing capabilities allow auditing multiple URLs simultaneously, while webhook integrations can trigger automatic audits when new pages are published. Structured Output components can generate audit reports in multiple formats including PDF, CSV for spreadsheet analysis, or JSON for integration with SEO management platforms. Advanced implementations might incorporate machine learning models trained on successful SEO implementations to provide more accurate recommendations.