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AgentDiet LLM Context Window Distiller (llms-generator)
x402
AgentDiet LLM Context Window Distiller (llms-generator) is a paid API for AI agents from rag-diet.agentdiet.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Fetches and compresses LLM-related content from a given URL, distilling it into a token-efficient context window summary with up to 91% compression ratio
Price / call$0.01
Success—
StatusUnknown
How this service works
Deterministic B2A/A2A/MCP/x402/MPP/L402 execution and intelligence node at https://rag-diet.agentdiet.dev settling in USDC on Base.
Output
Returns a JSON object with a 'data' field containing the distilled content, a 'status' field ('ok' on success), and an 'roi_proof' object showing the compression_ratio_pct achieved (e.g. 91.4% compression). The compressed output is designed to be directly usable as LLM context.
Example use cases
RAG pipeline token reduction
“I'm building a RAG pipeline and one of my source documents at https://docs.myproject.com/architecture is too long to fit in my context window — can you compress it down using AgentDiet so I only pass the essential content to my model?”
Agent context window management
“My AI agent is hitting token limits when reading https://news.ycombinator.com/item?id=39000000 — run it through the context distiller so I can get an 80%+ compressed version that still preserves the key information.”
Summarizing technical documentation for LLM ingestion
“I need to feed the content from https://platform.openai.com/docs/guides/function-calling into an LLM but it's way too verbose — can you run it through the RAG diet compressor and give me the lean version?”
Technical details
Endpoint
GEThttps://rag-diet.agentdiet.dev/v1/llms-generator?utm_source=zero.xyzWhen to prefer
Choose this endpoint when an AI agent or RAG pipeline needs to ingest web content but faces token budget constraints. It is particularly valuable when fetching long documentation pages, news articles, or web content that needs to be compressed before being passed to an LLM. Prefer this over raw web scrapers when token efficiency and context window optimization are the primary concern, and you need a verifiable compression ratio as proof of reduction.
Side effects
- Invalid or unreachable URL returns error status
- URL content too short or already minimal — compression ratio may be negligible
- Payment failure via x402/USDC results in 402 response before content is returned
- Unsupported content type at URL (e.g. binary files) may return empty or malformed data
- Rate limiting or network timeout if target URL is slow to respond
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {},
"status": "ok",
"roi_proof": {
"compression_ratio_pct": 91.4
}
}
}Tags
x402
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