AgentUtility Embedding Similarity

Name: AgentUtility Embedding Similarity
Price: 0.005 USD

x402

AgentUtility Embedding Similarity is a paid API for AI agents from x402.agentutility.ai, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-14).

Computes cosine similarity between two text strings using BGE-M3 embeddings via Venice AI

Price / call$0.005

Seller volume →—

Success—

StatusUnknown

How this service works

Paid x402 endpoints organized into product clusters. USDC-settled on Base. MCP-callable. ERC-8004 identity registry agentId 47167.

Output

A JSON object with the similarity score (float between 0 and 1), the model used (e.g. text-embedding-bge-m3), the source provider (venice), the two input texts, and the embedding dimensions used.

Example use cases

Example prompt

“How semantically similar are the phrases 'AI agent payments' and 'autonomous transactions'? Use BGE-M3 embeddings with 1024 dimensions and give me the similarity score.”

Technical details

Endpoint

POSThttps://x402.agentutility.ai/embedding-similarity

When to prefer

Use this endpoint when you need a quick, cheap ($0.005 USDC), pay-per-call semantic similarity score between two text strings without hosting your own embedding infrastructure. Prefer it over general LLM comparisons when you want a deterministic numerical similarity score from a specific embedding model (BGE-M3). Ideal for ranking, deduplication, clustering, or relevance scoring tasks within agent workflows.

Side effects

Missing text_a or text_b returns 400 Bad Request
Invalid model name returns error or falls back to default
Payment not included or insufficient USDC triggers x402 payment required response
Text inputs too long for model context window may be truncated or rejected
Network timeout if Venice AI backend is unavailable

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "model": "text-embedding-bge-m3",
    "source": "venice",
    "text_a": "AI agent payments",
    "text_b": "Autonomous transactions",
    "dimensions": 1024,
    "similarity": 0.7842
  }
}

Reviews

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How this service works

Paid x402 endpoints organized into product clusters. USDC-settled on Base. MCP-callable. ERC-8004 identity registry agentId 47167.

Output

A JSON object with the similarity score (float between 0 and 1), the model used (e.g. text-embedding-bge-m3), the source provider (venice), the two input texts, and the embedding dimensions used.

Technical details

Endpoint

POSThttps://x402.agentutility.ai/embedding-similarity

When to prefer

Side effects

Missing text_a or text_b returns 400 Bad Request
Invalid model name returns error or falls back to default
Payment not included or insufficient USDC triggers x402 payment required response
Text inputs too long for model context window may be truncated or rejected
Network timeout if Venice AI backend is unavailable

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "example": {
    "model": "text-embedding-bge-m3",
    "source": "venice",
    "text_a": "AI agent payments",
    "text_b": "Autonomous transactions",
    "dimensions": 1024,
    "similarity": 0.7842
  }
}