Firecrawl Architecture Variants

Overview

Three deployment architectures for Firecrawl at different scales: on-demand scraping for simple use cases, scheduled crawl pipelines for content monitoring, and real-time ingestion pipelines for AI/RAG applications. Choose based on volume, latency requirements, and cost budget.

Decision Matrix

Factor	On-Demand	Scheduled Pipeline	Real-Time Pipeline
Volume	< 500/day	500-10K/day	10K+/day
Latency	Sync (2-10s)	Async (hours)	Async (minutes)
Use Case	Single page lookup	Site monitoring	Knowledge base, RAG
Credit Control	Per-request	Per-crawl budget	Credit pipeline
Complexity	Low	Medium	High

Instructions

Architecture 1: On-Demand Scraping

User Request → Backend API → firecrawl.scrapeUrl → Clean Content → Response

Best for: chatbots, content preview, single-page extraction.

import FirecrawlApp from "@mendable/firecrawl-js";

const firecrawl = new FirecrawlApp({
  apiKey: process.env.FIRECRAWL_API_KEY!,
});

// Simple API endpoint
app.post("/api/scrape", async (req, res) => {
  const { url } = req.body;

  const result = await firecrawl.scrapeUrl(url, {
    formats: ["markdown"],
    onlyMainContent: true,
    waitFor: 3000,
  });

  res.json({
    title: result.metadata?.title,
    content: result.markdown,
    url: result.metadata?.sourceURL,
  });
});

// With LLM extraction
app.post("/api/extract", async (req, res) => {
  const { url, schema } = req.body;

  const result = await firecrawl.scrapeUrl(url, {
    formats: ["extract"],
    extract: { schema },
  });

  res.json({ data: result.extract });
});

Architecture 2: Scheduled Crawl Pipeline

Scheduler (cron) → Crawl Queue → firecrawl.asyncCrawlUrl → Result Store
                                                                  │
                                                                  ▼
                                                        Content Processor → Search Index

Best for: documentation monitoring, content indexing, competitive analysis.

import cron from "node-cron";

interface CrawlTarget {
  id: string;
  url: string;
  maxPages: number;
  paths?: string[];
  schedule: string; // cron expression
}

const targets: CrawlTarget[] = [
  { id: "docs", url: "https://docs.example.com", maxPages: 100, paths: ["/docs/*"], schedule: "0 2 * * *" },
  { id: "blog", url: "https://blog.example.com", maxPages: 50, schedule: "0 4 * * 1" },
];

// Schedule crawls
for (const target of targets) {
  cron.schedule(target.schedule, async () => {
    console.log(`Starting scheduled crawl: ${target.id}`);
    const job = await firecrawl.asyncCrawlUrl(target.url, {
      limit: target.maxPages,
      includePaths: target.paths,
      scrapeOptions: { formats: ["markdown"], onlyMainContent: true },
    });
    await db.saveCrawlJob({ targetId: target.id, jobId: job.id, startedAt: new Date() });
  });
}

// Separate worker polls for results
async function processPendingCrawls() {
  const pending = await db.getPendingCrawlJobs();
  for (const job of pending) {
    const status = await firecrawl.checkCrawlStatus(job.jobId);
    if (status.status === "completed") {
      await indexPages(job.targetId, status.data || []);
      await db.markComplete(job.id, status.data?.length || 0);
      console.log(`Crawl ${job.targetId} complete: ${status.data?.length} pages indexed`);
    }
  }
}
setInterval(processPendingCrawls, 30000);

Architecture 3: Real-Time Content Pipeline

URL Sources → Priority Queue → Firecrawl Workers → Content Validation
                                                          │
                                                          ▼
                                                   Vector DB + Search Index
                                                          │
                                                          ▼
                                                    RAG / AI Pipeline

Best for: AI training data, knowledge base, enterprise content platform.

import PQueue from "p-queue";

class ContentPipeline {
  private queue: PQueue;
  private firecrawl: FirecrawlApp;
  private creditBudget: number;
  private creditsUsed = 0;

  constructor(concurrency = 5, dailyBudget = 10000) {
    this.queue = new PQueue({ concurrency, interval: 1000, intervalCap: 10 });
    this.firecrawl = new FirecrawlApp({ apiKey: process.env.FIRECRAWL_API_KEY! });
    this.creditBudget = dailyBudget;
  }

  async ingest(urls: string[]) {
    if (this.creditsUsed + urls.length > this.creditBudget) {
      throw new Error("Daily credit budget exceeded");
    }

    // Use batch scrape for efficiency
    const result = await this.queue.add(() =>
      this.firecrawl.batchScrapeUrls(urls, {
        formats: ["markdown"],
        onlyMainContent: true,
      })
    );

    this.creditsUsed += urls.length;

    // Validate and process
    const pages = (result?.data || []).filter(page => {
      const md = page.markdown || "";
      return md.length > 100 && !/captcha|access denied/i.test(md);
    });

    // Store in vector DB
    for (const page of pages) {
      await vectorStore.upsert({
        id: page.metadata?.sourceURL,
        content: page.markdown,
        metadata: { title: page.metadata?.title, url: page.metadata?.sourceURL },
      });
    }

    return { ingested: pages.length, rejected: urls.length - pages.length };
  }

  async discover(siteUrl: string, pathFilter: string) {
    const map = await this.firecrawl.mapUrl(siteUrl);
    return (map.links || []).filter(url => url.includes(pathFilter));
  }
}

// Usage
const pipeline = new ContentPipeline(5, 10000);
const urls = await pipeline.discover("https://docs.example.com", "/api/");
const result = await pipeline.ingest(urls.slice(0, 100));
console.log(`Ingested ${result.ingested} pages into vector store`);

Choosing Your Architecture

Need real-time, user-facing response?
├── YES → On-Demand (Architecture 1)
└── NO → How many pages/day?
    ├── < 500 → On-Demand with caching
    ├── 500-10K → Scheduled Pipeline (Architecture 2)
    └── 10K+ → Real-Time Pipeline (Architecture 3)

Error Handling

Issue	Cause	Solution
Slow on-demand response	JS-heavy target page	Add caching layer, reduce waitFor
Stale indexed content	Crawl schedule too infrequent	Increase frequency for critical sources
Credit overrun	Pipeline ingesting too aggressively	Implement daily budget with hard cap
Duplicate content	Re-crawling same pages	Deduplicate by content hash before indexing

Resources

• Firecrawl API Reference
• Batch Scrape
• Crawl Endpoint

Next Steps

For common pitfalls, see

firecrawl-known-pitfalls

.