Firecrawl vs. Graphlit: Web Scraping Tool vs. Semantic Infrastructure

Firecrawl is a focused web scraping tool that converts websites into clean, LLM-ready Markdown. It handles JavaScript rendering, bypasses common blockers, and produces consistently clean output. For developers who need reliable web scraping, Firecrawl delivers.

Graphlit is a semantic infrastructure platform that includes web scraping as one of many capabilities. When you ingest a URL into Graphlit, we scrape it, convert to Markdown, AND process it through our full pipeline — embedding, entity extraction, knowledge graphs, and search.

Both tools can scrape websites. The difference is what happens after.

Table of Contents

1. TL;DR — Quick Comparison
2. What Firecrawl Does Well
3. What Graphlit Provides
4. The Overlap
5. When to Use Firecrawl
6. When to Use Graphlit
7. Integration Example

TL;DR — Quick Comparison

What Firecrawl Does Well

Firecrawl is a well-executed scraping tool:

Clean Markdown Output

Firecrawl excels at extracting the meaningful content from web pages and converting it to clean Markdown. Navigation, ads, and boilerplate are removed.

JavaScript Rendering

Modern websites are often SPAs or heavily JavaScript-dependent. Firecrawl renders pages properly before extraction.

Site Crawling

Give Firecrawl a URL and it can crawl an entire site, respecting sitemaps and following links intelligently.

Reliable Extraction

Handles edge cases, rotating proxies, and common anti-bot measures. Consistent results across different site types.

Developer-Friendly

Good API, clear documentation, easy to integrate. Python and JavaScript SDKs available.

LLM-Optimized

Output is specifically designed for LLM consumption — clean, structured, and ready for context injection.

For pure web scraping needs, Firecrawl is a solid choice.

What Graphlit Provides

Graphlit includes web scraping as part of comprehensive infrastructure:

Built-in Web Scraping

Ingest any URL and get clean Markdown. We handle JavaScript rendering, content extraction, and cleanup.

Site Crawling

Map and crawl entire sites with mapWeb, then ingest discovered pages.

Everything After Scraping

Every scraped page is automatically:

• Converted to clean Markdown
• Chunked semantically
• Embedded for vector search
• Entity-extracted (people, companies, topics)
• Connected to knowledge graphs
• Indexed for hybrid search

30+ Other Data Sources

Web scraping is one capability. Graphlit also ingests:

• Documents (PDFs, Office files)
• Communication (Slack, Discord, email)
• Development (GitHub, Linear, Jira)
• Media (podcasts, RSS, YouTube)
• Cloud storage (Drive, Dropbox, SharePoint)

Unified Knowledge Base

Scraped web content lives alongside everything else in a searchable, connected knowledge base.

RAG-Ready

Scraped content is immediately available for AI conversations with source citations.

The Overlap

Both tools scrape websites and produce Markdown. The overlap is real:

For basic web scraping, both work. The difference is scope.

When to Use Firecrawl

Choose Firecrawl when:

• Scraping is the whole job: You need Markdown output and nothing else
• Building custom pipelines: You have existing infrastructure for embeddings, search, etc.
• High-volume scraping: Dedicated scraping tool might be more cost-effective at scale
• Specific scraping features: Need screenshots, specific extraction modes, or Firecrawl-specific capabilities
• Web-only use case: No need for other data sources

Firecrawl does one thing well. If that's all you need, use it.

When to Use Graphlit

Choose Graphlit when:

• Building a knowledge base: Scraped content should be searchable and connected
• Multiple data sources: Need web + documents + Slack + email + more
• Entity extraction: Want to identify people, companies, topics from scraped content
• Knowledge graphs: Need relationships and connections across content
• RAG applications: Building AI that answers questions from scraped data
• Unified search: Query web content alongside everything else
• Team collaboration: Shared knowledge base across users

If scraping feeds into a larger AI application, Graphlit provides the infrastructure.

Integration Example

Firecrawl: Scrape to Markdown

from firecrawl import FirecrawlApp

app = FirecrawlApp(api_key="...")

# Scrape a single page
result = app.scrape_url("https://example.com/article")
markdown = result['markdown']

# Crawl a site
crawl_result = app.crawl_url(
    "https://example.com",
    params={'limit': 100}
)

# Now you have Markdown
# To build a knowledge base, you need to:
# 1. Store the content
# 2. Generate embeddings
# 3. Set up vector database
# 4. Extract entities
# 5. Build search index
# 6. Implement RAG
# 7. ...

Graphlit: Scrape to Knowledge Base

import { Graphlit, Types } from 'graphlit-client';

const client = new Graphlit();

// Scrape a single page — full processing automatic
const result = await client.ingestUri(
    "https://example.com/article",
    "Interesting Article"
);

// Content is now:
// - Scraped and cleaned
// - Embedded for vector search
// - Entities extracted
// - Knowledge graph connected
// - Search indexed

// Crawl a site
const siteMap = await client.mapWeb(
    "https://example.com",
    ["/blog/*", "/docs/*"],  // allowed paths
    ["/admin/*"]             // excluded paths
);

// Ingest discovered pages
for (const url of siteMap.mapWeb?.results || []) {
    await client.ingestUri(url);
}

// Or create a web feed for ongoing monitoring
const feed = await client.createFeed({
    name: "Example Site Monitor",
    type: Types.FeedTypes.Web,
    web: {
        uri: "https://example.com",
        includeFiles: false,
        readLimit: 100
    },
    schedulePolicy: {
        recurrenceType: Types.TimedPolicyRecurrenceTypes.Daily
    }
});

// Search across all scraped content
const contents = await client.queryContents({
    search: "relevant topic"
});

// RAG conversation with scraped sources
const response = await client.promptConversation(
    "What does this site say about X?",
    conversationId,
    { id: specificationId }
);

Summary

Firecrawl is a well-built web scraping tool. It produces clean Markdown from websites reliably and handles the hard parts of scraping (JavaScript, anti-bot, edge cases) well.

Graphlit includes web scraping as part of a complete semantic infrastructure platform. Scraping is built-in, but so is everything that comes after — embeddings, entities, knowledge graphs, search, and conversations.

Choose based on your needs:

• Just scraping? → Firecrawl is focused and does it well
• Building AI applications? → Graphlit provides the full stack

Both are good tools. They solve different-sized problems.

Explore Graphlit Features:

• Web Scraping and Search — Built-in web capabilities
• Content Ingestion — All ingestion options
• Building Knowledge Graphs — Entity extraction
• Data Connectors — 30+ source integrations

Learn More:

• Graphlit Documentation
• Firecrawl Documentation
• Schedule a Demo
• Join our Discord

Scraping extracts content. Infrastructure makes it useful.