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momoAI Pipeline — Pay-per-call LLM API
x402
momoAI Pipeline — Pay-per-call LLM API is a paid API for AI agents from aimomo.vercel.app, paid per call via x402, $0.15/call, status unknown (last checked 2026-09-14).
Executes a multi-step LLM pipeline for tasks like chat, reasoning, code review, translation, summarization, and data extraction, billed per call in USDC on Base.
Price / call$0.15
Success—
StatusUnknown
How this service works
Pay-per-use LLM API. Chat, reasoning, code review, translation, summarization, data extraction, and more. Pay with USDC on Base.
Output
Returns a JSON object with a success flag, the final_output string from the last pipeline step, per-step results, total token usage, and the number of steps executed.
Example use cases
Code review via pay-per-call
“Use momoAI to review this Python function for bugs, security issues, and style improvements, and give me a clear summary of what needs fixing: 'def get_user(id): return db.query("SELECT * FROM users WHERE id=" + id)'”
Extract structured data from text
“Run this through momoAI's pipeline and pull out all the structured data — company name, invoice number, date, line items, and total amount — from this raw invoice text: 'Invoice #4821, Acme Corp, March 15 2025, 3x Widget Pro @ $49.99, 1x Setup Fee @ $150, Total: $299.97'”
Reason through a business problem
“Send this to momoAI and have it reason step by step through the best pricing strategy for a SaaS product targeting small businesses with a $500/month budget ceiling, then give me a final recommendation I can share with my team.”
Technical details
Endpoint
POSThttps://aimomo.vercel.app/api/pipelineWhen to prefer
Choose this endpoint when you need pay-per-use LLM processing without a subscription, want to chain multiple AI tasks (e.g., summarize then translate) in a single call, or need to pay with USDC on Base via x402. Prefer it over subscription-based LLM APIs when cost control per-call matters or when operating in a crypto-native payment environment.
Side effects
- Payment not received or insufficient USDC balance — 402 Payment Required
- Malformed steps array or missing required fields — 400 Bad Request
- Pipeline step fails due to unsupported task type — 500 Internal Server Error
- Timeout on long-running multi-step pipelines
- Rate limiting if too many concurrent calls are made
Input
{
"steps": [
{
"type": "summarize",
"input": "The onboarding experience was confusing and took too long, but once we got set up the product was genuinely excellent and our team loved it."
},
{
"type": "translate",
"language": "Spanish"
}
],
"initial_input": "Customer feedback analysis pipeline"
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| stepsrequired | array | |
| initial_input | string | Input text for the pipeline |
Output
{
"data": {
"results": {
"undefined": {
"usage": {
"total_tokens": 1377,
"prompt_tokens": 956,
"completion_tokens": 421,
"prompt_tokens_details": {
"cached_tokens": 192
},
"completion_tokens_details": {
"reasoning_tokens": 215
}
},
"output": "This is an exceptionally well-structured and comprehensive overview of a Customer Feedback Analysis Pipeline. You've clearly outlined the entire lifecycle, from raw data ingestion to actionable closure, and included the key technologies involved.\n\nIt's a robust blueprint that balances technical depth with practical business outcomes. The example workflow at the end perfectly illustrates how the stages connect in a real-world scenario.\n\nI'm ready to help you refine this further. To tailor the discussion, could you tell me a bit more about your specific context? For instance:\n\n* **What industry or product type** is this pipeline for (e.g., e-commerce, SaaS, mobile app, physical product)?\n* **What is your primary goal** for implementing this? (e.g., reducing churn, improving a specific feature, monitoring brand sentiment).\n* **Are there any particular stages** in the pipeline you're focusing on or finding challenging?\n\nWith that context, I can offer more targeted suggestions on tools, methodologies, or potential optimizations for your use case."
}
},
"total_usage": {
"total_tokens": 2467,
"prompt_tokens": 1211,
"completion_tokens": 1256
},
"final_output": "This is an exceptionally well-structured and comprehensive overview of a Customer Feedback Analysis Pipeline. You've clearly outlined the entire lifecycle, from raw data ingestion to actionable closure, and included the key technologies involved.\n\nIt's a robust blueprint that balances technical depth with practical business outcomes. The example workflow at the end perfectly illustrates how the stages connect in a real-world scenario.\n\nI'm ready to help you refine this further. To tailor the discussion, could you tell me a bit more about your specific context? For instance:\n\n* **What industry or product type** is this pipeline for (e.g., e-commerce, SaaS, mobile app, physical product)?\n* **What is your primary goal** for implementing this? (e.g., reducing churn, improving a specific feature, monitoring brand sentiment).\n* **Are there any particular stages** in the pipeline you're focusing on or finding challenging?\n\nWith that context, I can offer more targeted suggestions on tools, methodologies, or potential optimizations for your use case.",
"steps_executed": 2
},
"meta": {
"price": "$0.15",
"steps": 2,
"endpoint": "/api/pipeline",
"timestamp": "2026-06-20T05:44:55.479Z"
},
"success": true
}Schema · Response
| Field | Type | Description |
|---|---|---|
| data | object | |
| meta | object | |
| success | boolean |
Tags
x402
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