Turn Any Website Into a Data Pipeline
The web data extraction CLI built for engineers. Point at any web resource. Get structured data back. Our AI agent builds the YAML data pipeline — you just describe what you need. Local storage. Zero token waste. Production-ready.
$ pip install anysite-cli
The Entire Web Is Your Database. The Agent Is Your Data Engineer.
Every website has structured data inside it. Anysite's AI extracts it — from any URL, any platform, any page. The CLI gives you a production runtime to build data pipelines against any web resource. And the Data Agent lets you skip the manual work entirely: describe what data you need in plain English, and the agent discovers endpoints, builds the YAML pipeline, estimates costs, and executes.
You're not choosing from a catalog. You're pointing at the web and getting data back.
Why Traditional Web Scraping Alternatives Fall Short
Every existing method for web data extraction has the same problem: they weren't built for production data pipelines.
Browser Automation
CSS selectors break on layout changes. Slow execution. Requires headless browsers and constant debugging.
Workflow Tools
n8n, Zapier, Make — every data transformation passes through LLM context. 10,000 records means millions of tokens.
Custom Scrapers
Weeks of development. Immediate maintenance burden. No standardized output. Every site needs unique logic.
API Aggregators
Fixed catalogs of endpoints. If the source you need isn't listed, you're stuck. No pipeline capabilities.
One Data Pipeline CLI. Seven Capabilities. Any Web Source.
Single API Calls
Instant requests with flexible output formats (JSON, CSV, JSONL, table) and field filtering. Dot-notation for nested data. Built-in presets.
anysite api /api/linkedin/user user=satyanadella --fields "name,headline,experience.title" anysite api /api/instagram/user user=natgeo --format table anysite api /api/twitter/user user=elonmusk --preset minimal
Batch Processing
Process thousands of inputs in parallel. Three error strategies: stop, skip, retry with backoff.
anysite api /api/linkedin/user \ --from-file users.txt --input-key user \ --parallel 5 --on-error skip --progress
Dataset Pipelines
Declarative YAML workflows with chained dependencies and scheduling. Six pre-built templates.
anysite dataset init prospect-pipeline anysite dataset collect pipeline.yaml --dry-run anysite dataset collect pipeline.yaml --incremental
Database Integration
Load into SQLite or PostgreSQL with auto-schema and diff-sync. Upsert with conflict handling.
anysite api /api/linkedin/user user=satyanadella \ | anysite db insert mydb --table profiles anysite db upsert mydb --table leads --conflict-key email
LLM Analysis
Classify, summarize, enrich, deduplicate using OpenAI or Anthropic. Four enrichment types. Built-in SQLite cache.
anysite llm classify dataset.yaml --source posts \ --categories "positive,negative,neutral" anysite llm enrich dataset.yaml --source companies \ --extract "industry_category,funding_stage" anysite llm dedupe dataset.yaml --source leads \ --threshold 0.85
SQL Querying
DuckDB SQL on collected datasets. Run analytics without external databases.
anysite dataset query pipeline.yaml \ --sql "SELECT * FROM employees WHERE title LIKE '%CTO%'"
Data Agent
The hero capability. Describe what data you need in natural language. The agent discovers endpoints, builds the pipeline YAML, estimates costs, and executes. Idea to structured dataset — zero config.
# Just describe what you need anysite agent "Find Series B SaaS companies, get their decision makers, and pull their recent LinkedIn posts"
Describe It or Define It. Collect. Store. Query.
Two paths to the same result: let the Data Agent build your pipeline from natural language, or write the YAML yourself for full control.
Define Pipeline
YAML config or natural language via Agent
Preview & Collect
Dry-run to estimate, then execute
Store Locally
Parquet, DuckDB, PostgreSQL, SQLite
Query & Analyze
SQL queries + LLM classification
name: prospect-pipeline sources: target_companies: endpoint: /api/linkedin/search/companies input: industry: "SaaS" employee_count: "51-200" parallel: 3 decision_makers: endpoint: /api/linkedin/company/employees depends_on: target_companies input: company: ${target_companies.urn} keywords: "VP Sales, Director Sales" count: 5 on_error: skip recent_posts: endpoint: /api/linkedin/user/posts depends_on: decision_makers input: urn: ${decision_makers.internal_id.value} count: 5 storage: format: parquet path: ./data/prospects
# Preview costs before running anysite dataset collect pipeline.yaml --dry-run # Execute the full pipeline anysite dataset collect pipeline.yaml # Run incremental updates anysite dataset collect pipeline.yaml --incremental # Query results with SQL anysite dataset query pipeline.yaml \ --sql "SELECT * FROM decision_makers WHERE title LIKE '%CTO%'" # Classify posts with LLM anysite llm classify pipeline.yaml --source recent_posts \ --categories "product_update,hiring,thought_leadership"
Any Website Is an Endpoint. Major Platforms Are Ready Out of the Box.
The Anysite engine turns any web page into structured data via AI parsing. Major platforms come with dedicated, optimized endpoints.
| Platform | What You Get | Example |
|---|---|---|
| Profiles, companies, posts, jobs, search, messaging, employees | anysite api /api/linkedin/user user=satyanadella |
|
| Twitter/X | Posts, threads, users, search, followers | anysite api /api/twitter/user user=elonmusk |
| Posts, reels, profiles, comments, likes | anysite api /api/instagram/user user=natgeo |
|
| Discussions, subreddits, comments, user history | anysite api /api/reddit/search/posts query="AI agents" |
|
| YouTube | Videos, channels, comments, subtitles | anysite api /api/youtube/video video_id=dQw4w9WgXcQ |
| SEC EDGAR | 10-K, 10-Q, 8-K filings | anysite api /api/sec/search/companies |
| Y Combinator | Companies, founders, batch data | anysite api /api/yc/search/companies |
| Search, Maps, News | anysite api /api/search/google |
| Capability | What It Does | Example |
|---|---|---|
| Web Parser | Any URL to structured JSON | anysite api /api/webparser/parse url="https://..." |
| AI Parsers | Specialized extraction for GitHub, Amazon, Glassdoor, G2, Trustpilot, Crunchbase, Pinterest, AngelList | anysite api /api/ai-parser/glassdoor url="..." |
| Data Agent | Describe a data need — agent discovers or creates the right endpoint | "Get pricing data from competitor websites" |
The endpoint library grows continuously. But you're never limited to it — the AI parser and Data Agent can extract structured data from any web resource.
Explore Available Endpoints
# Browse all ready-made endpoints anysite describe # Filter by platform anysite describe --search linkedin # Get parameter details for a specific endpoint anysite describe /api/linkedin/user
Built for Real Workflows
From lead gen to research, the CLI handles production-grade data collection.
Sales Intelligence
Define target criteria once. Pipeline refreshes on schedule via cron. Always-fresh prospect data flowing into your CRM.
Competitive Intelligence
Multi-source collection with anysite dataset diff for change detection across competitor websites.
Research at Scale
Batch processing 10K+ records with parallel execution and incremental tracking. Academic and market research workflows.
Brand Monitoring
Scheduled pipeline with LLM sentiment classification and webhook alerts. Know what's being said, automatically.
Prefer AI-native access? Try the MCP Server for Claude & Cursor. Need direct HTTP calls? Use the REST API. Compare all plans →
Your Data Stays Local. Your Tokens Stay Unburned.
Unlike workflow tools that pass every record through LLM context, Anysite CLI processes data locally. Only config enters the context window.
| Approach | 1,000 Records | 10,000 Records | 100,000 Records |
|---|---|---|---|
| Workflow Tool (context-based) | ~500K tokens | ~5M tokens | ~50M tokens |
| Anysite CLI | ~1K tokens | ~1K tokens | ~1K tokens |
| Efficiency gain | 500x | 5,000x | 50,000x |
Technical Specifications
Output Formats
JSON (default), JSONL, CSV, Rich table
Field Control
--fields, --exclude, --preset (minimal, contact, recruiting)
Error Handling
stop (default), skip, retry with backoff
LLM Support
OpenAI + Anthropic, 6 operations, SQLite response cache
Scheduling
Cron, systemd, webhooks
Install Extras
[data], [postgres], [llm], [all]
Unix Piping
# Pipe API output directly into database anysite api /api/linkedin/user user=satyanadella | anysite db insert mydb --table profiles # Pipe into jq for quick extraction anysite api /api/linkedin/company company=anthropic | jq '.employees[] | .title'
Simple Credit-Based Pricing
Start free. Scale as you grow. No rate limits on any plan.
PAYG top-ups from $20 (~15K credits). MCP Server also available at $30/mo unlimited.
Get Running in 5 Minutes
1. Install the CLI
pip install anysite-cli
2. Configure your API key
anysite config set api_key YOUR_API_KEY
3. Update the schema
anysite schema update
4. Make your first request
anysite api /api/linkedin/user user=satyanadella
5. Create your first pipeline
anysite dataset init my-first-pipeline anysite dataset collect my-first-pipeline/dataset.yaml --dry-run
Resources
Trusted by Data Teams
"Replaced 3 weeks of scraper code with one YAML file."
"Finally stopped burning tokens on data shuffling."
"The pipeline just runs. Haven't touched it in 2 months."
Turn Any Website Into Your Next Dataset
1,000 free credits. No credit card required. Every web resource is a potential data source — the agent handles the rest.
$ pip install anysite-cli