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ARBITER Semantic Ranking
x402
ARBITER Semantic Ranking is a paid API for AI agents from api.grip.fyi, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).
Ranks a fixed list of candidate strings against a query representing current state, intent, or perspective using deterministic semantic scoring.
Price / call$0.01
Success—
StatusUnknown
How this service works
ARBITER deterministic semantic control-flow and arbitration: measure and rank a fixed candidate field against current state, intent, or perspective.
Output
Returns a ranked list of up to top_k candidate strings ordered by their deterministic semantic relevance to the query, along with their associated scores or rankings indicating closeness of fit to the stated intent or state.
Example use cases
Routing agent decisions by context
“I have these possible next actions for my support bot: ['check order status', 'initiate return', 'connect to human', 'send FAQ link', 'schedule callback']. Rank them against the current user message 'my package never arrived and I want my money back' and give me the top 2.”
Selecting best prompt template
“I have 4 prompt templates for different writing styles — ['concise bullet points', 'formal paragraph', 'casual conversational', 'step-by-step numbered list'] — which one best fits the intent 'explain a technical error to a non-technical user'? Return the top 1.”
Prioritizing knowledge base articles
“Given a user's question 'how do I reset my 2FA device?', rank these 6 help articles — ['Account security overview', 'Resetting your password', 'Two-factor authentication setup', 'Billing and payments', 'Logging in on a new device', 'Contacting support'] — and show me the top 3 most relevant ones.”
Technical details
Endpoint
POSThttps://api.grip.fyi/v1/compareWhen to prefer
Choose this endpoint when you have a fixed, enumerable set of candidate strings and need to deterministically rank them against a query, intent, or current state — especially in agentic control-flow scenarios where you need reproducible, explainable ordering rather than generative output. Prefer this over embedding-based vector search when the candidate set is small and known in advance, and over LLM-based ranking when determinism and cost predictability matter.
Side effects
- Empty candidates array returns a validation error
- top_k larger than candidates array length may return fewer results
- Highly ambiguous or very short query strings may produce low-confidence rankings
- Non-string entries in candidates array may cause schema validation failures
- Network timeouts or payment failures (x402) may prevent results from being returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| query | string | Current state, intent, question, or perspective. |
| top_k | integer | |
| use_freq | boolean | |
| candidates | array | Fixed candidate field to measure and rank. |
Tags
x402
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