x402-deployer Content Moderation & Safety Classifier is a paid API for AI agents from x402-deployer.x402-deployer.workers.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).
Classifies text for toxicity and harmful content across default and custom categories, returning a verdict of allow, review, or block
Content moderation / safety classifier / OpenAI-style toxicity API. Morpheus primary, Venice fallback. Scores harassment, hate_speech, violence, sexual_content, self_harm, spam, phishing, doxing, illegal_activity, plus custom categories, and returns allow/review/block.
Returns per-category scores (e.g. harassment, hate_speech, violence, sexual_content, self_harm, spam, phishing, doxing, illegal_activity), a top-level verdict of allow/review/block, and any flagged categories with confidence signals.
POSThttps://x402-deployer.x402-deployer.workers.dev/moderate-contentUse this endpoint when you need a fast, structured safety verdict with named categories (including custom ones) and an actionable allow/review/block decision. Prefer it over generic LLM prompting for content moderation when you need consistent scoring, audit trails, or integration into automated content pipelines.
{
"input": {
"body": {
"text": "This is a great product! I really enjoyed using it and would recommend it to others.",
"custom_categories": [
"product_quality",
"recommendation"
]
},
"type": "http",
"method": "POST",
"bodyType": "json"
}
}| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
{
"model": "hermes-3-llama-3.1-405b",
"source": "morpheus",
"summary": "The text expresses a very positive sentiment about the product quality and includes a strong recommendation. No concerning content.",
"verdict": "allow",
"categories": {
"recommendation": {
"score": 0.9,
"flagged": true,
"evidence": "would recommend it to others"
},
"product_quality": {
"score": 0.9,
"flagged": true,
"evidence": "This is a great product! I really enjoyed using it"
}
},
"input_chars": 84,
"flagged_count": 2,
"categories_evaluated": [
"product_quality",
"recommendation"
]
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