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AgentDiet RAG Token Diet — Web URL Distiller
AgentDiet RAG Token Diet — Web URL Distiller is a paid API for AI agents from preflight.agentdiet.dev, paid per call via MPP, $0.001/call, status unknown (last checked 2026-10-21).
Fetches a webpage (or accepts raw HTML) and distills it into compact, agent-ready tokens using multiple extraction modes including BM25 budget-controlled summarization
Price / call$0.001
Success—
StatusUnknown
How this service works
Distill a URL or raw HTML payload with BM25 budget control (Live x402 v2, IETF MPP & MCP endpoint at https://preflight.agentdiet.dev/v1/rag-token-diet)
Output
Returns a JSON object containing the distilled content (as markdown, structured tables, skeleton outline, or BM25-ranked excerpt depending on mode), plus ROI proof metrics including raw token count, distilled token count, tokens saved, compression ratio percentage, and estimated LLM cost saved in USD. Also includes cache hit/miss status and billing details.
Example use cases
RAG pipeline context compression
“Fetch https://docs.openai.com/api-reference and distill it into structured markdown under 1000 tokens so I can include it in my agent's context window without blowing the budget.”
Topic-focused page excerpt
“Pull the content from https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture) and give me a BM25 budget excerpt focused on 'attention mechanism', capped at 500 tokens.”
Skeleton outline for navigation
“Grab https://developer.mozilla.org/en-US/docs/Web/JavaScript and distill it into a skeleton outline so my agent can understand the page structure before deciding what to fetch next.”
Technical details
Endpoint
GEThttps://preflight.agentdiet.dev/v1/rag-token-diet?utm_source=zero.xyzWhen to prefer
Use this endpoint when an AI agent needs to ingest web content but must stay within a strict token budget, or when feeding raw HTML into an LLM context would be wasteful. It is especially valuable in RAG pipelines where relevance-focused extraction (BM25 mode) is needed over simple truncation, or when structured/skeleton modes are needed to understand a page's layout before deeper processing. Prefer over generic scrapers when token economics and LLM cost savings matter.
Side effects
- URL is unreachable or returns non-200 status — endpoint may return an error status
- Raw HTML too large for processing — may time out or exceed internal limits
- BM25 query too vague — budget excerpt may be low quality or poorly focused
- max_tokens set too low — content may be severely truncated or empty
- Payment not provided or insufficient — HTTP 402 returned, no content delivered
- Invalid mode enum value — returns validation error
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
| Field | Type | Description |
|---|---|---|
| datarequired | object | Distilled Markdown, structured JSON tables/metadata, skeleton outline, or BM25 budget excerpt. |
| cache | object | |
| statusrequired | string | Execution status ('ok') |
| billing | object | |
| roi_proofrequired | object |
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