# ForgeMesh Embeddings API

> ForgeMesh 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 2026-09-14).

Generates 768-dimension embedding vectors for 1–64 texts per call, computed locally, paid per call with USDC via x402.

## Facts

- Endpoint: POST https://x402.forgemesh.io/embeddings
- Price: $0.002/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/forgemesh-embeddings-api-15230551
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_yC0pWNNMpffCE1Svu7WaA

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability forgemesh-embeddings-api-15230551 -d '<json body>'
```

Example prompt: Generate 768-dim embeddings for these three sentences so I can index them for semantic search: 'The cat sat on the mat.', 'A feline rested on a rug.', 'Dogs love playing fetch.' — pay per call with USDC, no OpenAI key needed.

## When to prefer this

Choose this endpoint when you need text embeddings without an OpenAI account or API key, want to pay per call in USDC with no subscription, require privacy (inputs are never stored), and need up to 64 texts vectorized in a single call at a low flat rate of $0.002 USDC.

## Known failure modes

- More than 64 texts submitted in one call — returns validation error
- Empty texts array or missing both 'text' and 'texts' fields — returns 400 bad request
- Payment not provided or insufficient USDC — returns 402 Payment Required
- Individual text too long for model context window — may return truncation error or 400
- Network timeout if batch is large and hardware is under load

## How this service works

Embeddings API: 768-dimension embedding vectors for up to 64 texts per call, computed locally on our hardware — no OpenAI key, no account, pay per call with USDC. Use for semantic search, similarity, clustering, deduplication, and RAG indexes. Inputs are never stored.

## Output

An array of 768-dimension floating-point embedding vectors, one per input text, suitable for semantic search, clustering, deduplication, or indexing into a vector database.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "text": {
   "type": "string",
   "description": "single text alternative to texts[]"
  },
  "texts": {
   "type": "array",
   "items": {
    "type": "string"
   },
   "description": "1-64 texts to embed"
  }
 }
}
```

## Response schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/forgemesh-embeddings-api-15230551/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from x402.forgemesh.io](https://www.zero.xyz/host/x402.forgemesh.io/llms.txt)