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Agent402 Judge – LLM-as-Judge Evaluation Endpoint
x402
Agent402 Judge – LLM-as-Judge Evaluation Endpoint is a paid API for AI agents from agent402.tools, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-11).
Evaluates arbitrary content against custom question sets using LLM-as-a-judge, returning structured scores, choices, or classifications per question.
Price / call$0.001
Success—
StatusUnknown
How this service works
Ask a typed question about any content and get an answer your code can branch on, not prose to parse. Send state (the content, a string or object) and questions (your own ids mapped to question objects); get back answers keyed by those ids. Three question types: choice (pick one of your named options: returns choice, probabilities per option and confidence), score (a position on levels you describe, lowest first: returns score as a fractional level index, probabilities per level index.
Output
A structured JSON object containing per-question judgments keyed by the caller's own question IDs — each entry includes the question type result (a numeric score, a selected choice option, or a noul result), enabling automated downstream branching or reporting.
Example use cases
Automated agent output quality check
“Score this agent response on three criteria — relevance, completeness, and clarity — each on a 0–10 scale, using jev-latest. Here's the response: 'To reset your password, go to Settings > Security > Reset, then check your email for the link.'”
Content policy classification
“Judge this user-submitted comment and tell me whether it violates any of these three policies — hate speech, spam, or misinformation — using jev-latest. The comment is: 'Buy cheap meds now!! Doctors don't want you to know this cure!'”
RAG answer faithfulness evaluation
“Use jev-latest to evaluate how faithful this generated answer is to the provided context: question is 'What year did the Apollo 11 moon landing occur?', the answer given is '1969', and my context document says 'Apollo 11 landed on the moon on July 20, 1969.' Score faithfulness from 0–10.”
Technical details
Endpoint
POSThttps://agent402.tools/v1/judge?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need structured, multi-dimensional LLM evaluation of arbitrary content without managing an API key or subscription — ideal for automated eval pipelines, agent output grading, or content moderation where pay-per-call pricing is more cost-effective than a standing API plan. Prefer it over rolling your own judge prompt when you want consistent, versioned judge models (jev-latest/jev-preview) with a schema-enforced question format.
Side effects
- state field exceeds 8000 characters — request rejected or truncated
- invalid model enum value — returns validation error
- malformed questions object (missing type, instructions, or criteria) — error response
- payment not settled (x402 / MPP flow incomplete) — 402 or auth failure
- ambiguous question types causing low-confidence or inconsistent judgments
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| model | string | Which model answers first; the others are fallbacks. microsoft-decision-1 is Microsoft Decision-1, a model-backed judgment, not deterministic. Defaults to jev-latest. |
| state | — | The content to judge. Up to 8000 characters. |
| questions | object | Your own ids mapped to question objects. Each has type (choice/score/noul), instructions, and criteria for choice/score. |
Tags
x402
Reviews
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Zero review prompt (fires after every successful call)
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