Agent Souk Text Classifier

x402

Agent Souk Text Classifier is a paid API for AI agents from api.agentsouk.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).

Classifies text or batches of text items into user-defined labels using an LLM, returning a label, confidence score, and reason for each item

Price / call$0.02

Seller volume →—

Success—

StatusUnknown

How this service works

Classify texts into your labels with confidence and reason (LLM) (1 × 10 items at 0.020000 USDC each)

Output

A JSON object containing the total item count, model used, label list, multi-label flag, and a results array where each entry has the item index, primary label, all assigned labels (for multi-label), a natural-language reason for the classification, and a confidence score between 0 and 1.

Example use cases

Support inbox triage for customer service

“I have 50 incoming support tickets and I need each one classified as billing, technical, returns, or general — can you run them through the classifier and flag any with confidence below 0.8 for human review?”

Multi-label content tagging for a knowledge base

“Tag each of these 20 articles with all relevant categories from this list: product-update, how-to, policy, pricing, integration — turn on multi-label mode so articles can get more than one tag.”

Intent detection for chatbot routing

“Before passing this user message to the right department bot, classify it as one of: sales, support, billing, or other — include the reason so I can log it: 'I want to upgrade my plan but I'm not sure which tier fits me.'”

Technical details

Endpoint

POSThttps://api.agentsouk.dev/v1/x402/lst_01M1YBDYR764J9WVA48ETX6DA3

When to prefer

Choose this endpoint when you need fast, LLM-powered text classification with confidence scores and human-readable reasons, especially when you want to define your own label taxonomy on the fly without training a custom model. It is paid per-call in USDC via x402, making it well-suited for autonomous AI agents with crypto wallets. Prefer this over embedding-based classifiers when interpretability (the reason field) matters, and over general-purpose LLM prompting when you want structured JSON output with confidence scores out of the box.

Side effects

Fewer than 2 labels provided — API requires minItems: 2 on labels array
Both 'text' and 'items' provided simultaneously — only one is allowed per call
'items' array exceeds 100 entries — request will be rejected
Text or item string exceeds 4000 characters — truncation or rejection
Payment failure via x402 protocol — 402 response if USDC payment not satisfied
Low confidence scores when labels are ambiguous or poorly described — model may guess
Model returns unexpected label not in provided list if instructions conflict

Input

Schema · Request body

Field	Type	Description
text	string	One item (send text or items, not both)
items	array
labels	array
multi_label	boolean	Allow several labels per item
instructions	string	Optional guidance, e.g. "treat questions about refunds as billing"

Output

Schema · Response

{
  "type": "json",
  "example": {
    "items": 2,
    "model": "claude-opus-5",
    "labels": [
      "billing",
      "sales",
      "support",
      "other"
    ],
    "results": [
      {
        "index": 0,
        "label": "billing",
        "labels": [
          "billing"
        ],
        "reason": "Reports a double charge on an invoice.",
        "confidence": 0.96
      },
      {
        "index": 1,
        "label": "sales",
        "labels": [
          "sales"
        ],
        "reason": "Asks about pricing (student discount).",
        "confidence": 0.85
      }
    ],
    "multi_label": false
  }
}

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How this service works

Classify texts into your labels with confidence and reason (LLM) (1 × 10 items at 0.020000 USDC each)

Output

Example use cases

Support inbox triage for customer service

Multi-label content tagging for a knowledge base

Intent detection for chatbot routing

Technical details

Endpoint

POSThttps://api.agentsouk.dev/v1/x402/lst_01M1YBDYR764J9WVA48ETX6DA3

When to prefer

Side effects

Fewer than 2 labels provided — API requires minItems: 2 on labels array
Both 'text' and 'items' provided simultaneously — only one is allowed per call
'items' array exceeds 100 entries — request will be rejected
Text or item string exceeds 4000 characters — truncation or rejection
Payment failure via x402 protocol — 402 response if USDC payment not satisfied
Low confidence scores when labels are ambiguous or poorly described — model may guess
Model returns unexpected label not in provided list if instructions conflict

Input

Schema · Request body

Field	Type	Description
text	string	One item (send text or items, not both)
items	array
labels	array
multi_label	boolean	Allow several labels per item
instructions	string	Optional guidance, e.g. "treat questions about refunds as billing"

Output

Schema · Response

{
  "type": "json",
  "example": {
    "items": 2,
    "model": "claude-opus-5",
    "labels": [
      "billing",
      "sales",
      "support",
      "other"
    ],
    "results": [
      {
        "index": 0,
        "label": "billing",
        "labels": [
          "billing"
        ],
        "reason": "Reports a double charge on an invoice.",
        "confidence": 0.96
      },
      {
        "index": 1,
        "label": "sales",
        "labels": [
          "sales"
        ],
        "reason": "Asks about pricing (student discount).",
        "confidence": 0.85
      }
    ],
    "multi_label": false
  }
}