SEO Platforms

Updated Feb 2026

Optimize websites for both AI assistants and traditional search engines. Firecrawl enables SEO platforms and consultants to analyze entire sites -- not just sample pages -- extracting metadata, headers, links, content structure, and technical health metrics at scale.

Why Firecrawl for SEO

SEO has shifted. Visibility in AI assistant responses is the new frontier of organic discovery, and traditional ranking factors are evolving. Firecrawl helps SEO teams adapt by providing comprehensive site analysis that covers both classic search optimization and AI readability.

Key Capabilities

• AI-first optimization -- Structure content for AI comprehension, not just search engine crawlers
• Full-site analysis -- Analyze every page, not just a sample, across thousands of URLs simultaneously
• Technical auditing -- Track broken links, redirect chains, canonical tag issues, and meta implementation
• Competitor benchmarking -- Extract competitor site structures, keyword usage, and content strategies
• Content gap analysis -- Identify unaddressed topics by analyzing competitor content organization

How It Works

Firecrawl Features Used

Feature	Role in SEO
Crawl	Ingest entire websites for comprehensive SEO analysis
Map	Discover all URLs and site structure for technical auditing
Scrape	Extract metadata, headers, and content from specific pages
Search	Research competitor rankings and content landscape
Change Tracking	Monitor your site and competitors for SEO-impacting changes

Technical SEO Audit

Crawl and Analyze Metadata

from firecrawl import Firecrawl

app = Firecrawl(api_key="fc-YOUR_API_KEY")

# Crawl the site with full metadata
result = app.crawl(
    url="https://yoursite.com",
    params={
        "limit": 2000,
        "scrapeOptions": {
            "formats": ["markdown", "links"]
        }
    }
)

# Analyze SEO metadata across all pages
seo_issues = []
for page in result["data"]:
    meta = page["metadata"]
    url = meta.get("url", "")

    # Check title length
    title = meta.get("title", "")
    if not title:
        seo_issues.append({"url": url, "issue": "Missing title tag"})
    elif len(title) > 60:
        seo_issues.append({"url": url, "issue": f"Title too long ({len(title)} chars)"})

    # Check description
    desc = meta.get("description", "")
    if not desc:
        seo_issues.append({"url": url, "issue": "Missing meta description"})
    elif len(desc) > 160:
        seo_issues.append({"url": url, "issue": f"Description too long ({len(desc)} chars)"})

    # Check for thin content
    content = page.get("markdown", "")
    word_count = len(content.split())
    if word_count < 300:
        seo_issues.append({"url": url, "issue": f"Thin content ({word_count} words)"})

print(f"Found {len(seo_issues)} SEO issues across {len(result['data'])} pages")
for issue in seo_issues[:20]:
    print(f"  {issue['url']}: {issue['issue']}")

Site Structure Analysis

# Map the site to analyze URL structure
site_map = app.map(url="https://yoursite.com")

# Analyze URL depth and structure
from urllib.parse import urlparse

depth_distribution = {}
for url in site_map["links"]:
    path = urlparse(url).path.strip("/")
    depth = len(path.split("/")) if path else 0
    depth_distribution[depth] = depth_distribution.get(depth, 0) + 1

print("URL Depth Distribution:")
for depth, count in sorted(depth_distribution.items()):
    bar = "#" * min(count, 50)
    print(f"  Depth {depth}: {count} pages {bar}")

Internal Link Analysis

# Analyze internal linking structure
internal_links = {}
for page in result["data"]:
    url = page["metadata"]["url"]
    links = page.get("links", [])

    # Count internal links per page
    internal = [l for l in links if "yoursite.com" in l]
    external = [l for l in links if "yoursite.com" not in l]

    internal_links[url] = {
        "internal_count": len(internal),
        "external_count": len(external),
        "total": len(links)
    }

# Find orphan pages (pages with few internal links pointing to them)
link_targets = {}
for page in result["data"]:
    for link in page.get("links", []):
        if "yoursite.com" in link:
            link_targets[link] = link_targets.get(link, 0) + 1

# Pages with fewer than 3 internal links pointing to them
orphans = [url for url, count in link_targets.items() if count < 3]
print(f"Potential orphan pages (< 3 internal links): {len(orphans)}")
for url in orphans[:10]:
    print(f"  {url}: {link_targets[url]} incoming links")

AI Readability Optimization

Structure your content for AI assistant comprehension:

# Analyze a page for AI readability
result = app.scrape(
    url="https://yoursite.com/product",
    params={
        "formats": ["json"],
        "jsonOptions": {
            "schema": {
                "type": "object",
                "properties": {
                    "has_structured_data": {"type": "boolean"},
                    "heading_hierarchy": {
                        "type": "array",
                        "items": {
                            "type": "object",
                            "properties": {
                                "level": {"type": "string"},
                                "text": {"type": "string"}
                            }
                        }
                    },
                    "has_faq_section": {"type": "boolean"},
                    "has_table_data": {"type": "boolean"},
                    "content_sections": {
                        "type": "array",
                        "items": {"type": "string"}
                    },
                    "key_entities": {
                        "type": "array",
                        "items": {"type": "string"}
                    }
                }
            }
        }
    }
)

ai_data = result["json"]

# Check AI readability factors
checks = []
if not ai_data.get("has_structured_data"):
    checks.append("Add structured data (Schema.org) for AI comprehension")
if not ai_data.get("has_faq_section"):
    checks.append("Add FAQ section for question-answer extraction")
if not ai_data.get("has_table_data"):
    checks.append("Add comparison tables for structured information")

heading_count = len(ai_data.get("heading_hierarchy", []))
if heading_count < 5:
    checks.append(f"Add more heading structure (currently {heading_count} headings)")

print("AI Readability Recommendations:")
for check in checks:
    print(f"  - {check}")

Competitor SEO Analysis

Benchmark Content Strategy

# Crawl competitor site for content analysis
competitor = app.crawl(
    url="https://competitor.com/blog",
    params={
        "limit": 200,
        "scrapeOptions": {
            "formats": ["markdown", "links"]
        }
    }
)

# Analyze competitor content strategy
topics = {}
for page in competitor["data"]:
    content = page.get("markdown", "")
    url = page["metadata"]["url"]
    word_count = len(content.split())

    # Categorize by URL path
    path = urlparse(url).path
    category = path.split("/")[2] if len(path.split("/")) > 2 else "uncategorized"
    topics[category] = topics.get(category, 0) + 1

print("Competitor Content Categories:")
for topic, count in sorted(topics.items(), key=lambda x: x[1], reverse=True):
    print(f"  {topic}: {count} articles")

Track Competitor SEO Changes

# Monitor competitor pages for SEO changes
competitor_pages = [
    "https://competitor.com",
    "https://competitor.com/product",
    "https://competitor.com/pricing",
]

for url in competitor_pages:
    result = app.scrape(
        url=url,
        params={"formats": ["markdown", "changeTracking"]}
    )

    if result.get("changeTracking", {}).get("changeStatus") == "changed":
        print(f"COMPETITOR SEO CHANGE: {url}")
        print(result["changeTracking"]["diff"][:300])

Content Gap Analysis

Identify topics your competitors cover that you do not:

# Map your site and competitor site
your_map = app.map(url="https://yoursite.com")
competitor_map = app.map(url="https://competitor.com")

your_paths = set()
for url in your_map["links"]:
    path = urlparse(url).path.lower()
    # Extract topic signals from URL paths
    for segment in path.split("/"):
        if segment and len(segment) > 3:
            your_paths.add(segment)

competitor_paths = set()
for url in competitor_map["links"]:
    path = urlparse(url).path.lower()
    for segment in path.split("/"):
        if segment and len(segment) > 3:
            competitor_paths.add(segment)

# Topics competitor covers that you do not
gaps = competitor_paths - your_paths
print(f"Content gaps (competitor has, you don't): {len(gaps)}")
for gap in sorted(gaps)[:20]:
    print(f"  - {gap}")

Multi-Region Rank Tracking

The FireGEO template enables tracking how your pages perform across different geographic regions:

# Check page visibility across regions
regions = ["New York", "London", "Tokyo", "Sydney"]

for region in regions:
    results = app.search(
        query="your target keyword",
        params={
            "limit": 10,
            "scrapeOptions": {"formats": ["markdown"]}
        }
    )

    # Check if your site appears in results
    your_results = [
        r for r in results["data"]
        if "yoursite.com" in r["metadata"]["url"]
    ]

    print(f"Region: {region}")
    print(f"  Your pages in top 10: {len(your_results)}")

Quick Start: FireGEO Template

The FireGEO template on GitHub provides multi-region rank tracking with:

• Geographic-specific search result extraction
• SERP feature detection (featured snippets, people also ask, etc.)
• Historical ranking trend analysis
• Competitor ranking comparison

Best Practices

1. Crawl the full site -- Sample-based audits miss issues; crawl everything with Crawl
2. Monitor change over time -- Use Change Tracking to detect when competitor SEO strategy shifts
3. Optimize for AI first -- Structure content with clear headings, FAQs, tables, and structured data for AI assistants
4. Build internal links -- Use link analysis to identify orphan pages and strengthen your site structure
5. Track content gaps -- Regularly compare your content coverage against top competitors
6. Automate audits -- Schedule weekly crawls to catch technical SEO issues before they impact rankings

Related Use Cases

• Competitive Intelligence -- Go deeper on competitor monitoring
• Content Generation -- Generate SEO-optimized content from competitive analysis
• AI Platforms & LLM Training -- Understand how AI systems consume your content