AI & Machine Learning
Collect Training Data
for Language Models
Building AI models requires massive amounts of high-quality training data. Spider crawls the web at scale, delivering clean, structured content ready for model training so you can focus on building, not scraping.
$ spider crawl example.com --format markdown --limit 1000
[crawl] Starting crawl of example.com
[crawl] Discovered 1,247 pages
[fetch] 200 OK /docs/getting-started 3.2kb
[fetch] 200 OK /docs/api-reference 8.7kb
[fetch] 200 OK /blog/scaling-llms 5.1kb
[fetch] 200 OK /blog/fine-tuning-tips 4.4kb
[clean] Stripping nav, ads, boilerplate...
[chunk] Splitting into 4,096-token chunks
[done] 1,000 pages / 3,891 chunks / 12.4MB
Ready for training pipeline.How It Works
From raw web to training-ready data
Spider handles every step of the data collection pipeline automatically.
Discover Pages
Submit a URL and Spider maps the entire site, following links, sitemaps, and navigation to discover every page.
Strip Noise
Navigation, ads, footers, and boilerplate are removed automatically. What remains is the actual content you need.
Split for Models
Content is chunked into token-optimized segments that respect heading boundaries and document structure.
Feed Your Model
Clean markdown with metadata, ready for fine-tuning, pre-training, or embedding generation. No post-processing needed.
Data Quality
Before and after Spider
- Building web scrapers from scratch is time-consuming
- Anti-bot measures block traditional crawlers
- Raw HTML requires extensive cleaning and parsing
- Scaling infrastructure is expensive and complex
- One API call to crawl entire websites
- Built-in anti-bot bypass with 99.9% success rate
- Clean markdown output ready for LLMs
- Unlimited concurrency to crawl at any scale
Features
Built for AI training pipelines
LLM-Ready Markdown
Get clean markdown output with proper formatting, headings, and structure. Feed it directly into your training pipeline without any post-processing. Spider preserves document structure while stripping all the noise.
Content Chunking
Automatically split content into optimally-sized chunks for embedding models and context windows. Chunk boundaries respect headings and paragraphs.
Metadata Extraction
Extract titles, descriptions, authors, dates, and structured data alongside the main content. Every piece of metadata your model needs.
Batch Processing
Submit thousands of URLs in a single request. Process entire datasets efficiently with concurrent crawling across all your target domains. Spider handles rate limiting, retries, and error recovery automatically.
Streaming Results
Stream results as they arrive. Start processing data before the entire crawl completes, keeping your pipeline moving without waiting.
Deduplication
Automatic URL normalization and content deduplication to avoid duplicate data in your training set. Cleaner datasets, better models.
Quick Start
Crawl a site in five lines
from spider import Spider
client = Spider()
# Crawl and get markdown content
result = client.crawl(
"https://example.com",
params={
"return_format": "markdown",
"limit": 1000, # Max pages to crawl
}
)
# Each page ready for training
for page in result:
content = page["content"]
url = page["url"]
# Add to your training dataset...Resources