Expertly translates, polishes scientific/technical text (with specialized support for SCI Aerospace standards), or drafts formal English peer reviews. Ensures accuracy, clarity, and adherence to high academic standards.
日本語の概要は準備中です。原文の説明を表示しています。
Complete knowledge domain for Firecrawl v2 API - web scraping and crawling that converts websites into LLM-ready markdown or structured data. Use when: scraping websites, crawling entire sites, extracting web content, converting HTML to markdown, building web scrapers, handling dynamic JavaScript content, bypassing anti-bot protection, extracting structured data from web pages, or when encountering "content not loading", "JavaScript rendering issues", or "blocked by bot detection". Keywords: firecrawl, firecrawl api, web scraping, web crawler, scrape website, crawl website, extract content, html to markdown, site crawler, content extraction, web automation, firecrawl-py, firecrawl-js, llm ready data, structured data extraction, bot bypass, javascript rendering, scraping api, crawling api, map urls, batch scraping
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Status: Production Ready ✅ Last Updated: 2025-10-24 Official Docs: https://docs.firecrawl.dev API Version: v2
Firecrawl is a Web Data API for AI that turns entire websites into LLM-ready markdown or structured data. It handles:
/v2/scrape - Single Page ScrapingScrapes a single webpage and returns clean, structured content.
Use Cases:
Key Options:
formats: ["markdown", "html", "screenshot"]onlyMainContent: true/false (removes nav, footer, ads)waitFor: milliseconds to wait before scrapingactions: browser automation actions (click, scroll, etc.)/v2/crawl - Full Site CrawlingCrawls all accessible pages from a starting URL.
Use Cases:
Key Options:
limit: max pages to crawlmaxDepth: how many links deep to followallowedDomains: restrict to specific domainsexcludePaths: skip certain URL patterns/v2/map - URL DiscoveryMaps all URLs on a website without scraping content.
Use Cases:
/v2/extract - Structured Data ExtractionUses AI to extract specific data fields from pages.
Use Cases:
Key Options:
schema: Zod or JSON schema defining desired structuresystemPrompt: guide AI extraction behaviorFirecrawl requires an API key for all requests.
fc-)NEVER hardcode API keys in code!
# .env file
FIRECRAWL_API_KEY=fc-your-api-key-here
# .env.local (for local development)
FIRECRAWL_API_KEY=fc-your-api-key-here
pip install firecrawl-py
Latest Version: firecrawl-py v4.5.0+
import os
from firecrawl import FirecrawlApp
# Initialize client
app = FirecrawlApp(api_key=os.environ.get("FIRECRAWL_API_KEY"))
# Scrape a single page
result = app.scrape_url(
url="https://example.com/article",
params={
"formats": ["markdown", "html"],
"onlyMainContent": True
}
)
# Access markdown content
markdown = result.get("markdown")
print(markdown)
import os
from firecrawl import FirecrawlApp
app = FirecrawlApp(api_key=os.environ.get("FIRECRAWL_API_KEY"))
# Start crawl
crawl_result = app.crawl_url(
url="https://docs.example.com",
params={
"limit": 100,
"scrapeOptions": {
"formats": ["markdown"]
}
},
poll_interval=5 # Check status every 5 seconds
)
# Process results
for page in crawl_result.get("data", []):
url = page.get("url")
markdown = page.get("markdown")
print(f"Scraped: {url}")
import os
from firecrawl import FirecrawlApp
app = FirecrawlApp(api_key=os.environ.get("FIRECRAWL_API_KEY"))
# Define schema
schema = {
"type": "object",
"properties": {
"company_name": {"type": "string"},
"product_price": {"type": "number"},
"availability": {"type": "string"}
},
"required": ["company_name", "product_price"]
}
# Extract data
result = app.extract(
urls=["https://example.com/product"],
params={
"schema": schema,
"systemPrompt": "Extract product information from the page"
}
)
print(result)
npm install @mendable/firecrawl-js
# or
pnpm add @mendable/firecrawl-js
# or use the unscoped package:
npm install firecrawl
Latest Version: @mendable/firecrawl-js v4.4.1+ (or firecrawl v4.4.1+)
import FirecrawlApp from '@mendable/firecrawl-js';
// Initialize client
const app = new FirecrawlApp({
apiKey: process.env.FIRECRAWL_API_KEY
});
// Scrape a single page
const result = await app.scrapeUrl('https://example.com/article', {
formats: ['markdown', 'html'],
onlyMainContent: true
});
// Access markdown content
const markdown = result.markdown;
console.log(markdown);
import FirecrawlApp from '@mendable/firecrawl-js';
const app = new FirecrawlApp({
apiKey: process.env.FIRECRAWL_API_KEY
});
// Start crawl
const crawlResult = await app.crawlUrl('https://docs.example.com', {
limit: 100,
scrapeOptions: {
formats: ['markdown']
}
});
// Process results
for (const page of crawlResult.data) {
console.log(`Scraped: ${page.url}`);
console.log(page.markdown);
}
import FirecrawlApp from '@mendable/firecrawl-js';
import { z } from 'zod';
const app = new FirecrawlApp({
apiKey: process.env.FIRECRAWL_API_KEY
});
// Define schema with Zod
const schema = z.object({
company_name: z.string(),
product_price: z.number(),
availability: z.string()
});
// Extract data
const result = await app.extract({
urls: ['https://example.com/product'],
schema: schema,
systemPrompt: 'Extract product information from the page'
});
console.log(result);
Scenario: Convert entire documentation site to markdown for RAG/chatbot
app = FirecrawlApp(api_key=os.environ.get("FIRECRAWL_API_KEY"))
docs = app.crawl_url(
url="https://docs.myapi.com",
params={
"limit": 500,
"scrapeOptions": {
"formats": ["markdown"],
"onlyMainContent": True
},
"allowedDomains": ["docs.myapi.com"]
}
)
# Save to files
for page in docs.get("data", []):
filename = page["url"].replace("https://", "").replace("/", "_") + ".md"
with open(f"docs/{filename}", "w") as f:
f.write(page["markdown"])
Scenario: Extract structured product data for e-commerce
const schema = z.object({
title: z.string(),
price: z.number(),
description: z.string(),
images: z.array(z.string()),
in_stock: z.boolean()
});
const products = await app.extract({
urls: productUrls,
schema: schema,
systemPrompt: 'Extract all product details including price and availability'
});
Scenario: Extract clean article content without ads/navigation
article = app.scrape_url(
url="https://news.com/article",
params={
"formats": ["markdown"],
"onlyMainContent": True,
"removeBase64Images": True
}
)
# Get clean markdown
content = article.get("markdown")
from firecrawl import FirecrawlApp
from firecrawl.exceptions import FirecrawlException
app = FirecrawlApp(api_key=os.environ.get("FIRECRAWL_API_KEY"))
try:
result = app.scrape_url("https://example.com")
except FirecrawlException as e:
print(f"Firecrawl error: {e}")
except Exception as e:
print(f"Unexpected error: {e}")
import FirecrawlApp from '@mendable/firecrawl-js';
const app = new FirecrawlApp({
apiKey: process.env.FIRECRAWL_API_KEY
});
try {
const result = await app.scrapeUrl('https://example.com');
} catch (error) {
if (error.response) {
// API error
console.error('API Error:', error.response.data);
} else {
// Network or other error
console.error('Error:', error.message);
}
}
onlyMainContent: true to reduce credits and get cleaner datamap endpoint first to plan crawling strategyThe Firecrawl SDK cannot run in Cloudflare Workers due to Node.js dependencies (specifically axios which uses Node.js http module). Workers require Web Standard APIs.
✅ Use the direct REST API with fetch instead (see example below).
Alternative: Self-host with workers-firecrawl - a Workers-native implementation (requires Workers Paid Plan, only implements /search endpoint).
This example uses the fetch API to call Firecrawl directly - works perfectly in Cloudflare Workers:
interface Env {
FIRECRAWL_API_KEY: string;
SCRAPED_CACHE?: KVNamespace; // Optional: for caching results
}
interface FirecrawlScrapeResponse {
success: boolean;
data: {
markdown?: string;
html?: string;
metadata: {
title?: string;
description?: string;
language?: string;
sourceURL: string;
};
};
}
export default {
async fetch(request: Request, env: Env): Promise<Response> {
if (request.method !== 'POST') {
return Response.json({ error: 'Method not allowed' }, { status: 405 });
}
try {
const { url } = await request.json<{ url: string }>();
if (!url) {
return Response.json({ error: 'URL is required' }, { status: 400 });
}
// Check cache (optional)
if (env.SCRAPED_CACHE) {
const cached = await env.SCRAPED_CACHE.get(url, 'json');
if (cached) {
return Response.json({ cached: true, data: cached });
}
}
// Call Firecrawl API directly using fetch
const response = await fetch('https://api.firecrawl.dev/v2/scrape', {
method: 'POST',
headers: {
'Authorization': `Bearer ${env.FIRECRAWL_API_KEY}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({
url: url,
formats: ['markdown'],
onlyMainContent: true,
removeBase64Images: true
})
});
if (!response.ok) {
const errorText = await response.text();
throw new Error(`Firecrawl API error (${response.status}): ${errorText}`);
}
const result = await response.json<FirecrawlScrapeResponse>();
// Cache for 1 hour (optional)
if (env.SCRAPED_CACHE && result.success) {
await env.SCRAPED_CACHE.put(
url,
JSON.stringify(result.data),
{ expirationTtl: 3600 }
);
}
return Response.json({
cached: false,
data: result.data
});
} catch (error) {
console.error('Scraping error:', error);
return Response.json(
{ error: error instanceof Error ? error.message : 'Unknown error' },
{ status: 500 }
);
}
}
};
Environment Setup: Add FIRECRAWL_API_KEY in Wrangler secrets:
npx wrangler secret put FIRECRAWL_API_KEY
Optional KV Binding (for caching - add to wrangler.jsonc):
{
"kv_namespaces": [
{
"binding": "SCRAPED_CACHE",
"id": "your-kv-namespace-id"
}
]
}
See templates/firecrawl-worker-fetch.ts for a complete production-ready example.
✅ Use Firecrawl when:
❌ Don't use Firecrawl when:
Cause: API key not set or incorrect Fix:
# Check env variable is set
echo $FIRECRAWL_API_KEY
# Verify key format (should start with fc-)
Cause: Exceeded monthly credits Fix:
onlyMainContent: true to reduce creditsCause: Page takes too long to load Fix:
result = app.scrape_url(url, params={"waitFor": 10000}) # Wait 10s
Cause: Content loaded via JavaScript after initial render Fix:
result = app.scrape_url(url, params={
"waitFor": 5000,
"actions": [{"type": "wait", "milliseconds": 3000}]
})
Perform interactions before scraping:
result = app.scrape_url(
url="https://example.com",
params={
"actions": [
{"type": "click", "selector": "button.load-more"},
{"type": "wait", "milliseconds": 2000},
{"type": "scroll", "direction": "down"}
]
}
)
result = app.scrape_url(
url="https://example.com",
params={
"headers": {
"User-Agent": "Custom Bot 1.0",
"Accept-Language": "en-US"
}
}
)
Instead of polling, receive results via webhook:
crawl = app.crawl_url(
url="https://docs.example.com",
params={
"limit": 1000,
"webhook": "https://your-domain.com/webhook"
}
)
| Package | Version | Last Checked |
|---|---|---|
| firecrawl-py | 4.5.0+ | 2025-10-20 |
| @mendable/firecrawl-js (or firecrawl) | 4.4.1+ | 2025-10-24 |
| API Version | v2 | Current |
Note: The Node.js SDK requires Node.js >=22.0.0 and cannot run in Cloudflare Workers. Use direct REST API calls in Workers (see Cloudflare Workers Integration section).
Token Savings: ~60% vs manual integration Error Prevention: API authentication, rate limiting, format handling Production Ready: ✅
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概要と使いどころ
Expertly translates, polishes scientific/technical text (with specialized support for SCI Aerospace standards), or drafts formal English peer reviews. Ensures accuracy, clarity, and adherence to high academic standards.
日本語の概要は準備中です。原文の説明を表示しています。
将中文学术文本翻译为符合国际期刊标准的高级英语,并提供多个润色版本以供选择。确保术语准确、逻辑严谨、句式复杂,彻底消除中式英语。
日本語の概要は準備中です。原文の説明を表示しています。
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
日本語の概要は準備中です。原文の説明を表示しています。
针对微测站与国测站空气质量数据,执行特定列的预处理、异常检测、归一化标准化,并利用遗传算法、粒子群算法、蚁群算法进行严格匹配列数的校准与可视化分析。
日本語の概要は準備中です。原文の説明を表示しています。
扮演人工智能辅助医生,结合最新AI工具(如医学成像、机器学习)和传统方法(如体检、实验室测试)来诊断病人症状的最可能原因。
日本語の概要は準備中です。原文の説明を表示しています。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
日本語の概要は準備中です。原文の説明を表示しています。