Riveter

Replaces scraper infrastructure with a single API – describe data needs via prompt and get self-evolving extraction workflows.

By Nathan KesslerUpdated May 8, 2026

Each tool is evaluated against our methodology using public docs, vendor demos, and hands-on testing.

Agentic extraction tools use AI models (often vision-language models) to autonomously understand and interact with web pages. Instead of writing CSS selectors or XPath queries, you describe what data you want in natural language and the AI figures out how to get it. This approach is more resilient to website changes and can handle complex, multi-step extraction workflows.

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Features

✓JS Rendering

✓Structured Output

—Open Source

—Self-Hosted Option

Pricing:PaidSee pricing →

Editorial assessment

The 'self-evolving workflows' concept is compelling – extraction logic that adapts when target sites change, reducing maintenance burden. KYC/risk data use cases show enterprise traction. Early-stage with limited public documentation. The self-evolving claim needs real-world validation at scale. Niche positioning around compliance/KYC data may limit broader adoption.

How Riveter compares

Diffbot

Diffbot's knowledge graph provides similar adaptive extraction with 10+ years of production validation.

parse.bot

parse.bot is simpler for static extraction needs, while Riveter targets dynamic, changing sites.

See full Riveter alternatives comparison →

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