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AgentDiet Prompt Guard — Context Window Distiller
x402
AgentDiet Prompt Guard — Context Window Distiller is a paid API for AI agents from rag-diet.agentdiet.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-22).
Compresses and sanitizes prompt/context text to reduce token usage by up to 91% before sending to an LLM
Price / call$0.005
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 compressed/distilled output, a 'status' field ('ok' on success), and an 'roi_proof' object including 'compression_ratio_pct' showing how much the input was reduced (e.g. 91.4% compression).
Example use cases
Slash LLM costs before inference
“Before sending this 3,000-token system prompt to GPT-4, compress it with AgentDiet's distiller so I'm not wasting money on bloat — here's the text: 'You are an AI assistant for Acme Corp. You help customers with billing, returns, shipping, product questions, and general support. Always be professional...'”
RAG context trimming for agent pipeline
“I've got a huge retrieved document chunk I need to pass into my RAG pipeline — can you run it through the AgentDiet prompt-guard to squeeze it down and show me the compression ratio? The chunk is the full FAQ page text I just retrieved.”
Sanitize and compress agent system prompt
“My agent's system prompt has grown to over 2,000 tokens and it's getting expensive. Use AgentDiet to distill and de-bloat it, and tell me what compression ratio you achieved.”
Technical details
Endpoint
GEThttps://rag-diet.agentdiet.dev/v1/prompt-guard?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to reduce token count of prompts or context windows before passing them to an LLM, especially in high-volume agentic pipelines where LLM API costs matter. It is particularly well-suited for RAG pipelines, A2A agent chains, or MCP-based orchestration where context windows grow large. Prefer it over manual prompt truncation when you want a measurable compression ratio and deterministic output.
Side effects
- Missing required 'input' field returns validation error
- Empty or missing 'text' query parameter may return empty or trivial compression result
- Payment failure via x402/USDC on Base blocks the request
- Malformed input type or method enum returns schema validation error
- Network timeout if the distillation pipeline is slow on very large inputs
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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