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AgentDiet RAG-Diet Mesh Dispatch — Context Window Distiller
x402
AgentDiet RAG-Diet Mesh Dispatch — Context Window Distiller 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).
Compresses and distills AI context windows via a query-based mesh dispatch, reducing token usage by up to 85-91% 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 'status' field ('ok'), a 'data' object containing the distilled/compressed output, and an 'roi_proof' object including a 'compression_ratio_pct' value (e.g. 91.4%) showing how much the context was reduced.
Example use cases
Token cost reduction for RAG pipelines
“I'm spending too much on LLM inference because my RAG results are bloated — can you send this query through the AgentDiet distiller and compress the retrieved context before I pass it to the model? Query: 'what are the main risk factors in this contract?'”
Context window compression for multi-agent workflow
“My agent-to-agent pipeline keeps hitting context limits. Can you use the AgentDiet mesh dispatch to distill and compress the context for this step? The query is: 'extract action items from today's meeting notes'.”
ROI proof on context compression
“I want to see how much I can actually save on tokens — can you dispatch a query through AgentDiet's context distiller and return the compression ratio proof? The query is: 'find all compliance requirements in this policy document'.”
Technical details
Endpoint
GEThttps://rag-diet.agentdiet.dev/v1/mesh/dispatch?utm_source=zero.xyzWhen to prefer
Choose this endpoint when your AI agent or pipeline needs to reduce token usage before passing retrieved or aggregated context to an LLM, particularly in RAG, A2A, or MCP workflows where context windows are tight or inference costs are high. It is especially useful when you need verifiable compression metrics (roi_proof) to demonstrate token savings. Prefer it over manual truncation or summarization when deterministic, structured compression with a proof of reduction is required and you are operating in a USDC-on-Base micropayment-capable environment.
Side effects
- Missing required 'input' object or 'type'/'method' fields returns a validation error
- Unsupported HTTP method enum value (not GET or POST) causes rejection
- Payment not settled in USDC on Base results in 402 Payment Required
- Empty or malformed query string may return empty or minimal distilled output
- Network or node mesh failure may return non-ok 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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