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AgentDiet Context Bloat Eliminator
x402
AgentDiet Context Bloat Eliminator is a paid API for AI agents from rag-diet.agentdiet.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-22).
Compresses and distills AI context windows by removing bloat, reducing token usage by up to 85-91% while preserving semantic meaning.
Price / call$0.001
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 distilled/compressed context, a 'status' field confirming success, and an 'roi_proof' object that includes a compression_ratio_pct value (e.g. 91.4%) demonstrating how much token bloat was eliminated.
Example use cases
RAG pipeline token cost reduction
“I'm feeding a bunch of web pages into my RAG pipeline and it's using way too many tokens. Can you compress the content at https://docs.example.com/api-reference using the context bloat eliminator so I only pass the essential information to the LLM?”
Long article distillation for agent reasoning
“Before my agent tries to reason over this 50,000-token news article at https://news.example.com/long-report, can you use AgentDiet to strip out all the bloat and give me just the dense, meaningful content? I want to see the compression ratio too.”
Multi-agent pipeline context optimization
“My multi-agent workflow keeps hitting context limits. Can you run https://internal.wiki/project-spec through the context bloat eliminator to shrink it down before passing it between agents? I need the distilled version and the ROI proof showing how much was compressed.”
Technical details
Endpoint
GEThttps://rag-diet.agentdiet.dev/v1/context-bloat-eliminator?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to reduce token consumption before passing fetched web content or documents into an LLM or multi-agent pipeline. It is particularly valuable for RAG systems, A2A workflows, and MCP integrations where context window size is a bottleneck and cost efficiency matters. Prefer it over manual summarization when you want deterministic, reproducible compression with a measurable compression ratio proof.
Side effects
- Invalid or unreachable URL returns an error with no data
- Unsupported HTTP method (not GET or POST) causes schema validation failure
- Payment not settled in USDC on Base results in 402 response blocking access
- Content at URL is already minimal with no bloat to remove, resulting in low compression ratio
- Missing required 'input' field causes schema validation error
- Mode parameter not recognized may result in default behavior or error
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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