ManyLives Review Reply Generator

x402

ManyLives Review Reply Generator is a paid API for AI agents from api.manylives.me, paid per call via x402, $0.004/call, status unknown (last checked 2026-10-13).

Generates a professional, fact-checked reply to a customer review for a business, with sentiment analysis and escalation guidance

Price / call$0.004

Seller volume →—

Success—

StatusUnknown

How this service works

Pay-per-call tools for people building AI agents: live web search and page contents, point-in-time crypto market data and news for backtests (no lookahead), verifiable track records of 1,000 crypto strategies locked on the Base network, company profiles, quick AI and batch prices. Ask us to turn a public website into a JSON endpoint. Pay per call, no account, free trial.

Output

Returns a JSON object containing the drafted reply text, a list of compliance checks performed (e.g. no admission of fault, no invented facts), identified review topics, overall sentiment classification, whether the review warrants immediate escalation, and the AI model used to generate the reply.

Example use cases

Responding to a negative restaurant review

“Someone left a 1-star review for my restaurant, Harbour Table, saying: 'Food was cold and service was terrible on Friday night.' Our facts: Fridays are our busiest night and we added extra staff last week. Draft a warm reply under 500 characters and tell them to call us at 03 8888 1234 if they want to follow up.”

Formal reply for a hotel complaint

“I manage a boutique hotel called The Pines and we got a 2-star review saying 'Room smelled musty and checkout was rushed.' Our policy is that guests can request late checkout 24 hours in advance. Can you write a formal reply under 600 characters? Include our email contact@thepines.com.”

Brief acknowledgment of a mixed review

“Can you draft a brief reply for my bookshop, Inkwell & Co., to this 3-star review: 'Good selection but staff seemed distracted and the line was long.' Keep the tone brief and friendly, under 300 characters.”

Technical details

Endpoint

POSThttps://api.manylives.me/paid/text/review-reply?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a compliance-aware, fact-grounded business reply to a customer review — especially when you want built-in checks against fault admission, invented offers, or unsupported claims. It is ideal for reputation management agents handling Google, Yelp, or Tripadvisor reviews at scale with pay-per-call pricing and no account required. Prefer it over generic LLM prompting when you need structured output with sentiment, escalation signals, and topic extraction alongside the draft reply.

Side effects

Missing required 'review' field returns a validation error
Review text exceeding 4,000 characters is rejected
max_chars outside the 150–1500 range may cause unexpected truncation or errors
Insufficient USDC balance results in 402 Payment Required before processing
Model hallucination of facts not present in the provided 'facts' field (partially mitigated by code checks)
Very ambiguous or extremely short reviews may produce generic replies

Input

Schema · Request body

Field	Type	Description
tone	string	optional warm (default), brief or formal
facts	string	optional: the business's own facts and policies (hours, refund policy, what changed), up to 10,000 characters
rating	string	optional star rating 1-5
review	string	the review text (required, up to 4,000 characters)
contact	string	optional: how unhappy customers should reach you, e.g. 'call 02 9999 0000' or 'email hello@yourbusiness.com'
business	string	business name
max_chars	string	optional 150-1500 (default 600)

Output

Schema · Response

{
  "type": "json",
  "example": {
    "reply": "Thank you for letting us know, and we're sorry your meal arrived cold after a long wait. Saturday lunch was busier than usual and we've since added a second cook on weekends. We'd like to hear more: please call us on 02 9999 0000. – Harbour Cafe",
    "checks": [
      "numbers and times found in your facts or the review",
      "no admission of fault or liability",
      "no offers beyond your facts",
      "no links beyond your contact"
    ],
    "topics": [
      "wait time",
      "cold food"
    ],
    "sentiment": "negative",
    "drafted_by": "open model (llama-3.3-70b), checked by code",
    "escalate_now": false
  }
}

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