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AgentDiet Alpha Stream — AI Context Window Distiller
x402
AgentDiet Alpha Stream — AI Context Window Distiller is a paid API for AI agents from status.agentdiet.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Streams a distilled, token-compressed version of a target HTTP resource, reducing context window usage by up to 91% for AI agents
Price / call$0.01
Success—
StatusUnknown
How this service works
Deterministic B2A/A2A/MCP/x402/MPP/L402 execution and intelligence node at https://status.agentdiet.dev settling in USDC on Base.
Output
Returns a JSON object containing a compressed 'data' field with the distilled content, a 'status' field ('ok' on success), and an 'roi_proof' object including the compression_ratio_pct (e.g. 91.4%) demonstrating how much the context was reduced.
Example use cases
Compress large docs for agent pipelines
“I need to ingest this long research paper at https://arxiv.org/abs/2401.00001 into my agent's context — can you run it through AgentDiet's alpha-stream to shrink it down and tell me how much compression we got?”
Reduce token costs on live web data
“Before you analyze this news article at https://reuters.com/article/xyz, distill it through AgentDiet first to cut the token count — I want the de-bloated context and the ROI proof showing the compression ratio.”
Fit API responses into tight context budgets
“The REST API at https://api.myservice.com/data returns huge JSON — can you stream it through AgentDiet's context distiller using a GET request so we only use a fraction of the tokens when you process the response?”
Technical details
Endpoint
GEThttps://status.agentdiet.dev/v1/alpha-stream?utm_source=zero.xyzWhen to prefer
Choose this endpoint when your AI agent needs to ingest large web pages, documents, or API responses but faces context window constraints or token cost pressure. It is particularly valuable in A2A and MCP pipelines where multiple agents pass context between each other and every token counts. Prefer it over raw fetch-and-summarize when you need a deterministic, verifiable compression ratio (roi_proof) rather than a lossy LLM-generated summary.
Side effects
- Missing or invalid 'target' query parameter returns an error or empty data
- Target URL is unreachable or returns non-200, resulting in upstream fetch failure
- Payment not settled in USDC on Base causes x402 payment-required error
- Input method enum mismatch (only GET/POST allowed) causes schema validation failure
- Service node unavailability causes non-200 status from the alpha-stream endpoint
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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