ManyLives Transaction Categoriser

x402

ManyLives Transaction Categoriser is a paid API for AI agents from api.manylives.me, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-29).

Categorises bank or payment transaction rows from CSV data into labelled expense/income categories using a combination of rules and AI

Price / call$0.005

Seller volume →—

Success—

StatusUnknown

How this service works

Pay-per-call tools for people building AI agents: live web search and page contents, prompt-injection checks, market data and risk sizing for trading agents, company profiles and quick AI. Ask us to turn a public website into a JSON endpoint. Pay per call, no account, free trial.

Output

Returns a JSON object with the total number of rows processed, counts of rows categorised by rule vs AI, per-category amount totals, and a labelled array of each transaction row with its assigned category, amount, description, and the method used (rule or ai).

Example use cases

Monthly bookkeeping expense sort

“I've got my business bank statement as a CSV with columns date, description, and amount — can you categorise every transaction into income, software_subscriptions, payroll, and marketing so I can hand it to my accountant?”

Freelancer invoice vs fee separation

“Here's my payment processor export as a CSV. Categorise all the rows and total up how much was income from clients versus how much was eaten up by card processing fees.”

Startup spending analysis by type

“Run this CSV of transactions through the transaction categoriser and group them into income, ad_spend, saas_tools, and other_expenses — I want to see the totals for each category so I can review our burn rate.”

Technical details

Endpoint

POSThttps://api.manylives.me/paid/data/categorise-transactions?utm_source=zero.xyz

When to prefer

Choose this endpoint when you have raw bank statement or payment processor CSV data and need each transaction row labelled with an expense or income category — especially useful for bookkeeping automation, accounting agent workflows, or expense reporting pipelines. It combines rule-based fast categorisation with AI fallback, making it accurate for both common patterns (like 'CARD PROCESSING FEES') and ambiguous descriptions. Prefer it over general-purpose LLM calls when you want structured JSON output with per-category totals and per-row attribution at a fixed low cost per call.

Side effects

Missing or malformed CSV/rows input returns an error
Unrecognisable column names with no overrides cause auto-detection failure
Ambiguous transaction descriptions may be miscategorised by the AI component
Very large CSVs may incur multiple per-call charges or hit size limits
Invalid or empty category list causes fallback to default categories

Input

Schema · Request body

Field	Type	Description
csv	string	CSV text with a header row
rows	string	array of {description, amount} (or csv)
categories	string	optional list of your own category names to use instead of ours
amount_column	string	optional column name (auto-detected: amount or value, or separate paid-in and paid-out columns)
description_column	string	optional column name (auto-detected: description, memo, details, narrative, payee)

Output

Schema · Response

{
  "type": "json",
  "example": {
    "rows": 3,
    "by_ai": 1,
    "totals": {
      "income": 2400,
      "payment_processing_fees": -31.2
    },
    "by_rule": 2,
    "labelled": [
      {
        "by": "rule",
        "row": 1,
        "amount": -31.2,
        "category": "payment_processing_fees",
        "description": "CARD PROCESSING FEES"
      },
      {
        "by": "ai",
        "row": 2,
        "amount": 2400,
        "category": "income",
        "description": "ACME PTY LTD INV 4410"
      }
    ]
  }
}

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