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AgentDiet Grounded Citations
x402
AgentDiet Grounded Citations 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).
Retrieves grounded, compressed citations from a RAG-distilled context window, reducing token usage by up to 91% while preserving semantic fidelity.
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 compressed, grounded citation results, a 'status' field confirming success, and an 'roi_proof' object showing the compression_ratio_pct (e.g. 91.4%) — demonstrating how much of the original context was eliminated while preserving semantic grounding.
Example use cases
Token-efficient research retrieval
“I'm building an agent that needs to answer questions about climate policy, but my context window keeps overflowing — can you pull grounded citations on 'Paris Agreement carbon targets' through AgentDiet's RAG distiller so I get compressed, cited results instead of a bloated raw corpus?”
Cost reduction for high-volume agents
“My AI pipeline is making hundreds of RAG lookups a day and the token costs are killing me — use AgentDiet's grounded-citations endpoint to retrieve compressed references for the query 'FDA drug approval process' and show me the compression ratio so I can see the savings.”
Cited answer grounding for QA bot
“I need my QA bot to give users properly sourced answers without dumping a wall of text into the prompt — fetch grounded citations for 'how does RLHF work' through AgentDiet's RAG distiller and give me the distilled result with the ROI proof.”
Technical details
Endpoint
GEThttps://rag-diet.agentdiet.dev/v1/grounded-citations?utm_source=zero.xyzWhen to prefer
Choose this endpoint when your AI agent needs to retrieve cited, RAG-grounded information while aggressively minimizing token usage — especially in high-frequency pipelines where context window costs are significant. It is uniquely suited for agents operating under strict token budgets that still require sourced, verifiable answers rather than raw retrieved chunks. Prefer it over generic RAG retrieval when you need an auditable compression ratio (roi_proof) alongside the result.
Side effects
- Missing required 'input' object returns 400 validation error
- Invalid HTTP method enum value (not GET or POST) causes schema rejection
- Empty or missing 'query' param returns empty data with no citations
- Payment not settled in USDC on Base results in 402 Payment Required
- Downstream RAG pipeline unavailability returns 503 or error status
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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