The entire web is your database

Structured data from any website — via API, MCP, or CLI

Describe the data you need. The agent builds the pipeline. Any website becomes structured, queryable data — flowing into your databases on schedule.

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01
Describe
02
Discover
03
Collect
04
Store
The Problem

The web wasn't built for machines

4.7B
web pages indexed
99.7%
unstructured HTML
~0
machine-ready
What happens when you try
Write scraper
→
It works!
→
Site changes layout
→
Pipeline breaks
→
Fix scraper
→
It works again!
→
Rate limited
→
Build proxy layer
→
Maintain 14 scripts
→
Hire someone to maintain them
→
They quit
What happens with Anysite
Describe what you need
→
Structured JSON
60+ ready-made endpoints
Any URL AI-parsed on demand
Self-healing adapts when sites change
Process

From description to database

Step 01

Describe what you need

Plain English or YAML. "I need decision makers at Series B SaaS companies and their recent LinkedIn activity."

Step 02

Agent discovers and builds

Finds endpoints, chains data sources, estimates cost. You approve — it runs.

Step 03

Data flows into your database

Structured JSON into SQLite, PostgreSQL, or ClickHouse. Auto-schema. LLM enrichment built in.

Step 04

Refreshes on schedule

One cron expression. Incremental tracking. Webhook on completion.

YAML Pipeline
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
Your Agent + Anysite CLI
You: "I need decision makers at Series B SaaS
     companies and their recent LinkedIn activity"

Agent: Discovering endpoints...
       Building pipeline: companies → employees → posts
       Estimated cost: ~2,400 credits
       Proceed? [y/n]

Agent: Collecting companies (47 found)...
       Mapping to employees (312 contacts)...
       Fetching post history...
       Storing in Parquet → ./data/prospects/

Done. 312 records. Query with:
anysite dataset query pipeline.yaml
Coverage

Pre-built where it matters. AI-powered for everything else.

in LinkedIn

Profiles, companies, posts, jobs, search, email finder.

𝕏 Twitter / X

Profiles, tweets, search with engagement filters.

ig Instagram

Posts, reels, comments, followers.

r/ Reddit

Subreddits, posts, comments, user history.

▶ YouTube

Videos, channels, comments, subtitles.

$ SEC EDGAR

10-K, 10-Q, 8-K filings.

</> GitHub

Repos, profiles, code metadata.

a Amazon

Products, reviews.

d DuckDuckGo

Web search results.

cb Crunchbase

Company profiles, funding, investors, search.

* Any URL

AI parser. Any web page → structured JSON.
Use Cases

What teams build with this

Prospect databases that refresh overnight

Define ICP in YAML. Pipeline runs nightly. CRM stays current.

Track competitors across every signal

Monitor LinkedIn, Twitter, Reddit, YouTube. Diff between runs.

10,000 records, zero extraction code

Batch + parallel + incremental. LLM enrichment built in.

Give your agents reliable data access

Structured JSON, consistent schemas, agent-native protocol.

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Architecture

1,000 tokens, not 50 million

Context-window approach
~50M
tokens
Web pages piped through LLM
Scales with data size
Anysite approach
~1K
tokens
Collection happens locally
Same cost at 10 or 100K records

Typical research workflow across 50 web pages. Browser-based approach puts raw HTML into LLM context (~1M tokens per page). Anysite: the LLM sees only the config; collection runs on Anysite infrastructure.

Start Building See Pricing
Access

One engine, four interfaces

MCP to explore. CLI to execute. Same engine underneath.

MCP Server — Explore data conversationally. $30/mo unlimited.

Learn more →

CLI — Production pipelines in YAML. Open source. pip install anysite-cli

Learn more →

REST API — Direct HTTP access. One API key. Anysite CLI (pip install anysite-cli) or any HTTP client.

Learn more →

n8n — Visual automation. Drag-and-drop. No code.

Learn more →
Pricing

Start with MCP, scale with credits

MCP Unlimited

$30/month
  • Unlimited MCP requests (fair use: 50K/month)
  • 5 meta-tools — LinkedIn, Twitter, Instagram, Reddit, YouTube, SEC EDGAR, and any URL
  • Person Analyzer and Competitor Analyzer skills
  • Works with Claude Desktop, Claude Code, Cursor, ChatGPT
  • Rate limit: 6 requests/minute
Get MCP Unlimited
First month free with code MCP30

Credit plans unlock the REST API and CLI at scale. All plans include MCP access and full platform coverage.

Plan Price/mo Credits Rate Limit
Starter $49 15,000 60 req/min Start trial →
Growth $200 100,000 90 req/min Get started →
Scale $300 190,000 150 req/min Get started →
Pro $549 425,000 200 req/min Get started →
Enterprise $1,199+ 1.2M+ 200 req/min Contact us →
Starter includes 7-day trial with 1,000 credits. Add-on: pay-as-you-go top-ups at $2.90/1K credits with any active subscription.

The web is the world's largest database.
Start querying it.

Pre-built endpoints for the platforms that matter. AI parsing for everything else. One engine, your choice of interface.

Get API Key Read the Docs
GitHub Documentation hello@anysite.io