ForgeMesh Text Embeddings API

x402

ForgeMesh Text Embeddings API is a paid API for AI agents from x402.forgemesh.io, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).

Converts up to 64 texts per call into 768-dimensional embedding vectors for semantic search, clustering, deduplication, and RAG pipelines, billed per call via USDC over x402.

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

Text embeddings API without an API key: up to 64 texts per call, 768-dimension embedding vectors computed on our own hardware. For semantic search, clustering, dedup, and RAG. Pay per call with USDC over x402; inputs never stored.

Output

An array of 768-dimensional float vectors, one per input text, suitable for cosine similarity, nearest-neighbor search, clustering, or as input features to downstream ML models.

Example use cases

Example prompt

“Can you embed these 10 product descriptions into 768-dimensional vectors so I can index them for semantic search: 'wireless noise-cancelling headphones', 'ergonomic office chair', 'standing desk converter', 'USB-C hub', 'mechanical keyboard', 'monitor arm', 'webcam 4K', 'desk lamp LED', 'cable management box', 'laptop stand'?”

Technical details

Endpoint

POSThttps://x402.forgemesh.io/text-embeddings

When to prefer

Choose this endpoint when you need text embeddings without registering for or managing an API key, want to pay per call in USDC rather than a monthly subscription, need a 768-dimension vector space suitable for standard RAG and semantic search pipelines, or want assurance that inputs are not stored. Prefer this over OpenAI or Cohere embeddings when keyless, pay-as-you-go crypto billing is required.

Side effects

More than 64 texts in a single call — API likely rejects or truncates the batch
Empty texts array or missing both 'text' and 'texts' fields — validation error
Payment failure via x402 (insufficient USDC balance or bad payment header) — 402 response
Input text too long for the underlying model — may truncate or error
Network timeout on large batches processed on their own hardware

Input

Schema · Request body

Field	Type	Description
text	string	single text alternative to texts[]
texts	array	1-64 texts to embed

Output

Schema · Response

{
  "type": "json",
  "example": {
    "model": "nomic-embed-text",
    "dimensions": 768,
    "embeddings": [
      [
        0.011,
        -0.02
      ],
      [
        0.03,
        0.004
      ]
    ]
  }
}

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How this service works

Output

An array of 768-dimensional float vectors, one per input text, suitable for cosine similarity, nearest-neighbor search, clustering, or as input features to downstream ML models.

Example use cases

Example prompt

Technical details

Endpoint

POSThttps://x402.forgemesh.io/text-embeddings

When to prefer

Side effects

More than 64 texts in a single call — API likely rejects or truncates the batch
Empty texts array or missing both 'text' and 'texts' fields — validation error
Payment failure via x402 (insufficient USDC balance or bad payment header) — 402 response
Input text too long for the underlying model — may truncate or error
Network timeout on large batches processed on their own hardware

Input

Schema · Request body

Field	Type	Description
text	string	single text alternative to texts[]
texts	array	1-64 texts to embed

Output

Schema · Response

{
  "type": "json",
  "example": {
    "model": "nomic-embed-text",
    "dimensions": 768,
    "embeddings": [
      [
        0.011,
        -0.02
      ],
      [
        0.03,
        0.004
      ]
    ]
  }
}