AgentDiet Semantic Distillation API

x402

AgentDiet Semantic Distillation API is a paid API for AI agents from agentdiet-72343955726.us-central1.run.app, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-13).

Fetches and distills a webpage URL or raw HTML into a compact, token-efficient representation (Markdown, structured JSON, skeleton outline, or BM25-pruned budget excerpt) to minimize LLM token consumption.

Price / call$0.001

Seller volume →—

Success—

StatusUnknown

How this service works

15-App Autonomous Business-to-Agent (B2A) API Hub & MCP Suite. Supports Web Distillation, On-Chain Risk, DEX Token Intelligence, B2B Lead Enrichment, Domain OSINT, Email Verification, SEC Filings, GitHub Audits, Prompt Firewall, and native HTTP 402 (x402 Base USDC).

Output

Returns a 'data' object containing the distilled content in the requested format (Markdown text, structured JSON with tables and metadata, a skeleton outline, or a BM25-pruned excerpt), plus an 'roi_proof' object showing raw token count, distilled token count, tokens saved, compression ratio percentage, and estimated LLM cost saved in USD. Optionally includes cache status (HIT/MISS) and billing details.

Example use cases

Token-budget RAG pipeline preprocessing

“Before you send the content of https://docs.openai.com/api-reference to the LLM, distill it in budget mode focused on the query 'rate limits' with a max of 2000 tokens so I don't blow my context window.”

Structured data extraction from product page

“Fetch https://www.bestbuy.com/site/apple-macbook-pro-16/product123.p and distill it in structured mode so I get the specs, price tables, and metadata as clean JSON without all the HTML noise.”

Skeleton outline of documentation site

“Give me a skeleton outline of the page at https://docs.stripe.com/payments so I can see the high-level structure and headings without reading the full content.”

Technical details

Endpoint

GEThttps://agentdiet-72343955726.us-central1.run.app/v1/distill?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to feed webpage content into an LLM and want to minimize token usage and cost before doing so. It is especially valuable when processing long documentation pages, product pages, or any HTML-heavy URLs where raw content would exceed context limits or be wasteful. Prefer it over generic scrapers when you need token-budget control (BM25 pruning with max_tokens), structured extraction of tables and metadata, or a skeleton outline of page structure. The built-in ROI proof (tokens saved, cost saved) makes it easy to justify in cost-sensitive agent pipelines.

Side effects

URL is unreachable or returns a non-200 HTTP response, causing a fetch failure
HTML is empty or too minimal to distill meaningfully, returning sparse output
mode=budget used without providing a 'query' parameter, resulting in an error or fallback behavior
max_tokens set too low relative to the content, producing a heavily truncated result
Payment not provided or insufficient for L402/x402 micropayment, resulting in HTTP 402 rejection
Rate limiting or timeout on the target URL causing a delayed or failed fetch

Input

Schema · Request body

Field	Type	Description
urlrequired	string	Target webpage URL to fetch and distill (or builtin://stripe-docs)
html	string	Optional raw HTML string to distill directly without fetching
mode	string	Distillation mode
query	string	BM25 focus query when mode=budget
max_tokens	integer	Strict token budget ceiling when mode=budget

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {
      "mode": "markdown",
      "title": "Example Documentation",
      "content": "# Example Documentation\nClean agent-ready markdown..."
    },
    "status": "ok",
    "roi_proof": {
      "raw_tokens": 14200,
      "tokens_saved": 13520,
      "roi_multiplier": 67.6,
      "distilled_tokens": 680,
      "compression_ratio_pct": 95.2
    }
  }
}

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