AgentDiet RAG Context Window Distiller — Workflow Executor

Name: AgentDiet RAG Context Window Distiller — Workflow Executor
Price: 0.01 USD

x402

AgentDiet RAG Context Window Distiller — Workflow Executor 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).

Executes a deterministic context-compression workflow that distills and de-bloats AI context windows, reducing token usage by up to 91% via RAG-based distillation.

Price / call$0.01

Seller volume →—

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

A JSON object containing a 'data' field with the compressed/distilled output, a 'status' field indicating success, and an 'roi_proof' object showing the compression_ratio_pct achieved (e.g. 91.4%), confirming how much the context was reduced.

Example use cases

LLM cost reduction for RAG pipelines

“My RAG pipeline is pulling in way too many tokens per query and my OpenAI bill is out of control — can you run the AgentDiet context distiller on this query to compress the retrieved chunks before I send them to the model?”

Long-document Q&A context trimming

“I have a huge document I want to ask questions about but it won't fit in the context window — use AgentDiet to distill and compress the content down so it fits, using the query 'summarize the main legal obligations in this contract'.”

A2A agent pipeline token budget enforcement

“My agent pipeline is hitting token limits when passing context between agents — can you run the AgentDiet workflow executor with a GET on the query 'extract action items from this meeting transcript' to compress the context before the next agent step?”

Technical details

Endpoint

GEThttps://rag-diet.agentdiet.dev/v1/workflows/execute?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to programmatically compress or distill large retrieved context (RAG chunks, documents, agent memory) before passing it to an LLM, especially when token budgets are tight or LLM API costs are a concern. It is particularly suited for A2A and agentic pipeline workflows where context is passed between nodes and must be kept under a token ceiling. Prefer it over generic summarization endpoints when you need a deterministic compression ratio proof and micropayment-settled ($0.01 USDC) per-call billing on Base.

Side effects

Missing required 'input' object or 'type'/'method' fields returns a validation error
Invalid method enum value (not GET or POST) causes schema rejection
Empty or malformed query string may result in low-quality distillation output
Payment failure or insufficient USDC balance returns a 402 Payment Required response
Service unavailability or upstream workflow node errors may return a 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
    }
  }
}

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