The Problem

The web wasn't built for machines

The web was designed for humans, then optimized for SEO, then locked down against bots. The gap between what AI agents need and what the web gives them is wide enough to be its own category of work — and most teams treat it as a one-time build. It isn't.

The maintenance loop

01 Write a scraper — it works, until it doesn't

→

02 Breaks overnight — a site update, a DOM change, a new anti-bot layer

→

03 Fix the scraper — half a day on a problem that isn't your actual work

→

04 Hit rate limits — now you need a proxy layer on top

→

05 14 scripts across 14 platforms — each with its own failure mode and update cycle

→

06 Hire someone to maintain it — they leave; you're back to square one

Web data access is still unsolved infrastructure. The answer isn't a faster scraper — it's a different abstraction: treat websites as APIs.

The Solution

Any website. Structured JSON. No maintenance.

Describe what you need — in plain English or YAML. Anysite turns it into a production data pipeline.

Hundreds of ready-made endpoints

The platforms you actually use, pre-built and maintained.

AI parsing for any URL

Point the engine at any public website and it generates a structured endpoint on demand.

Self-healing

When a site changes its DOM, the extraction layer adapts; your code doesn't change.

Full pipeline included

Extract, transform, store, schedule — not just an API call, a complete data infrastructure layer.

This is the data layer underneath autonomous agents — the role Stripe plays for payments or Twilio plays for communications. The unit of value is structured, reliable data, not raw HTML.

How It Works

Four steps from description to flowing data

1

Describe

Write what you need in plain English or YAML. No scraper logic, no selectors, no boilerplate.

pipeline.yaml

name: prospect-pipeline
description: "Find VP Engineering titles at B2B SaaS companies in SF, enrich each profile, and refresh weekly."
storage:
  destination: postgresql
  table: prospects
schedule: "0 2 * * 1"  # every Monday at 2am

2

Your agent discovers and builds

Your AI agent reads the description, uses endpoint discovery to identify the right APIs, chains the sources together, and estimates credit cost before running.

agent session

Your agent: I'll search LinkedIn for VP Engineering titles filtered to B2B SaaS + San Francisco.
            Then enrich each profile with experience, skills, and email lookup.
            Estimated: ~12 credits per prospect. Run?

3

Data flows into your database

Structured JSON lands in your database with consistent field names. PostgreSQL, SQLite, ClickHouse, or flat files — pick your destination in the YAML.

prospects.json

{
  "name": "Jane Smith",
  "headline": "VP of Engineering at Acme Corp",
  "location": "San Francisco, CA",
  "email": "jane@acme.com",
  "experience": [...],
  "skills": ["Python", "Distributed Systems", "..."],
  "collected_at": "2026-03-12T02:14:33Z"
}

4

Refreshes on schedule

Set a cron expression. Anysite tracks what it's already collected, runs incremental updates, and fires a webhook when complete. Your database stays current without manual runs.

Sources

Hundreds of sources. Growing every week.

Pre-built endpoints for the platforms you know — plus AI parsing for everything else.

The pre-built endpoints are the convenience layer. The AI engine is the product: point it at any public URL and it generates a structured endpoint on demand. New sources ship continuously.

Social

Instagram, Twitter / X, Reddit, YouTube

Professional & B2B

LinkedIn (profiles, companies, people search, jobs, posts), Crunchbase

Commerce

Amazon (products, prices, offers, reviews), eBay

Finance & Filings

SEC EDGAR (10-K, 10-Q, 8-K), JPX

Search

DuckDuckGo, general web results

Maps & Local

Google Maps (places, reviews)

Developer & Startup

GitHub, Hacker News, Stack Exchange, Y Combinator, Product Hunt

Any URL

The AI parser turns any public webpage into structured JSON, no pre-built endpoint required.

See every supported source →

What Teams Build

Real pipelines, real output

Describe the outcome. The agent builds it. Here's what comes back.

"Compare the live buy-box price of one product across every EU Amazon storefront."

Same product (Logitech MX Master 3S, ASIN B0FHHV6YR5), five storefronts, one query.

Storefront	Price (EUR)
amazon.fr	88.80
amazon.de	99.99
amazon.it	99.99
amazon.es	99.99
amazon.nl	99.99

amazon.fr came back €11.19 below the other four — a live promotion the catalog price didn't show. A comparison that takes 15 minutes of tab-switching runs in seconds, on schedule, exported to one file. (Prices captured 13 Jun 2026; storefronts drift independently.)

"Find VP Engineering at B2B SaaS companies in SF, score each against our ICP, land them in our CRM by morning."

Every match enriched and scored 0–100 against your ICP — LinkedIn title + company stage + hiring signals + email — deduplicated and ranked.

result

{
  "name": "Jordan Lee",
  "title": "VP Engineering",
  "company": "Acme SaaS — Series B, 180 employees",
  "lead_score": 87,
  "signals": ["hiring 4 backend roles", "Python/AWS stack", "ICP title match"],
  "email": "jordan@acmesaas.com"
}

Re-runs every Monday with incremental tracking — no duplicate records.

"Track a competitor's posts, open roles, and pricing-page changes — alert me when anything moves."

Every public signal on one competitor, collected to a single table. When their site changes, your table updates too.

result

{
  "competitor": "competitor.com",
  "new_job_posts_7d": 6,
  "linkedin_posts_7d": 9,
  "headcount_delta_30d": "+14",
  "pricing_change": "Pro tier $99 → $119"
}

"Brief me on Acme Corp before the call — funding, filings, team, and what people are saying."

Crunchbase funding + SEC filings + LinkedIn leadership + cross-platform sentiment, assembled into one structured brief.

result

{
  "company": "Acme Corp",
  "funding": "$42M Series B — lead investor Sequoia, 2025-11",
  "sec_filings": ["10-K (2025)", "8-K (2026-03)"],
  "leadership": [{ "role": "CEO", "tenure": "4y" }, { "role": "CFO", "tenure": "2y" }],
  "sentiment_30d": { "reddit": "positive", "twitter": "mixed" }
}

"Map every senior ML engineer in Berlin and rank by skill match."

The whole market segment — title, skills, and experience — structured for ranking and outreach. Market mapping, not one-off lookups.

result

{
  "market": "Senior ML Engineer · Berlin",
  "candidates_found": 214,
  "ranked_by": "skill_match",
  "top_skills_in_pool": ["PyTorch", "LLMs", "Kubernetes"],
  "top_match": { "current_company": "Acme", "skill_match": 0.91 }
}

"What are r/ClaudeAI, Twitter, and YouTube saying about MCP this week?"

Posts and comments across platforms, filtered and aggregated server-side — sentiment-ready records, not a context-window flood.

result

{
  "topic": "MCP",
  "window": "last 7 days",
  "posts_analyzed": 1240,
  "sentiment": { "positive": 0.62, "neutral": 0.27, "negative": 0.11 },
  "top_themes": ["tool discovery", "auth setup", "context limits"]
}

Tab 1 is a real, dated API run. Tabs 2–6 show representative output shapes for each workflow.

Token Efficiency

Collection happens on our infrastructure — not through your LLM

When AI agents browse the web through an LLM, every page burns tokens. A typical research workflow across 50 pages can run into the millions of tokens. Through Anysite, your LLM sees clean JSON, not raw HTML — collection runs on our infrastructure, not in your context window.

Same collection cost at 10 records or 100,000 It doesn't scale with your LLM.
Structured output from the start No LLM overhead for parsing or cleaning.
Server-side filter, aggregate, group Raw records never flood your AI's context window.
Predictable credit usage 1 credit per standard request, regardless of page complexity.

Three Interfaces

Same engine, three ways to access it

Interface	Best for
MCP Server	Explore data conversationally in Claude, Cursor, ChatGPT — 5 meta-tools over hundreds of endpoints
CLI	Production pipelines from the terminal — declarative YAML, batch, schedule. Open source (MIT)
HTTP / REST API	Integrate into applications — hundreds of pre-built endpoints, consistent JSON schemas

One engine across MCP, CLI, and REST. Anysite also plugs into visual workflow tools when you'd rather wire it in than write code.

MCP Server Learn more →

Connect Anysite to your AI assistant and explore data through natural language. 5 meta-tools (discover, execute, get_page, query_cache, export_data) reach hundreds of endpoints across LinkedIn, Instagram, Twitter, Reddit, YouTube, Amazon, and any URL.

CLI Learn more →

Production-grade pipelines in declarative YAML. Run from any server, schedule with cron, store results in your database. Open source under MIT license.

terminal

# Install
pip install anysite-cli

# Run a pipeline
anysite run prospect-pipeline.yaml

# Or query directly
anysite api /api/linkedin/user user=satyanadella with_experience=true

HTTP / REST API Learn more →

One authentication header. Hundreds of pre-built endpoints. Consistent JSON schemas across every platform.

terminal

curl -X POST "https://api.anysite.io/api/linkedin/user" \
  -H "access-token: YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"user": "satyanadella", "with_experience": true}'

The recommended client is the Anysite CLI (pip install anysite-cli). Any HTTP client works for direct access.

Pricing

Start with MCP. Scale into production when you need it.

One plan ladder. MCP access is included on every plan — $30 flat is just the entry.

MCP Unlimited

$30/mo

Unlimited MCP requests, no credit counting, no usage anxiety (fair use 50K req/mo)
Works with Claude, Cursor, ChatGPT, and any MCP client
The flat, MCP-only on-ramp — move up the ladder when you need more

Get MCP Unlimited

When you need the REST API, the CLI, or production volume, move up the ladder — every plan below includes MCP access too. Credits are shared across all of them.

Plan	Price/mo	Credits	Rate Limit
Starter	$49	15,000	60 req/min	Start trial →
Growth Most popular	$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 a 7-day free trial with 1,000 credits. Standard endpoints cost 1 credit per request. Pay-as-you-go top-ups are $2.90 / 1K credits (active subscription required, credits roll over 12 months). Enterprise adds dedicated infrastructure and white-glove support — contact hello@anysite.io for volume pricing.

See full pricing →

The entire web is your database

The web wasn't built for machines

Any website. Structured JSON. No maintenance.

Hundreds of ready-made endpoints

AI parsing for any URL

Self-healing

Full pipeline included

Four steps from description to flowing data

Describe

Your agent discovers and builds

Data flows into your database

Refreshes on schedule

Hundreds of sources. Growing every week.

Social

Professional & B2B

Commerce

Finance & Filings

Search

Maps & Local

Developer & Startup

Any URL

Real pipelines, real output

Collection happens on our infrastructure — not through your LLM

Same engine, three ways to access it

MCP Server Learn more →

CLI Learn more →

HTTP / REST API Learn more →

Start with MCP. Scale into production when you need it.

MCP Unlimited

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