Head to head · Assistants reached by message · October 2026 research run

folk vs Zapia

folk scores 19.8 (F) on agent readiness against Zapia's 17.3 (F), and leads in 3 of 7 scored categories. Zapia leads on maintenance & community. Both do assistants reached by message.

Best AI personal assistants that act for one person · All 834 assistants comparisons

Which one, for what

folk F

Best for People who want a texting companion for habits, workouts, study and money goals, with a free tier.

Ahead on

  • Reliability, 25 against 15
  • Schema & documentation, 10 against 0
  • Transparency & trust, 67 against 59

Also in its favour

  • Free to start without a card

Watch for

No API, MCP server or A2A endpoint for an outside agent.

Zapia F

Best for Spanish- and Portuguese-speaking users who want an assistant over their mail, calendar, Drive and WhatsApp in an app.

Ahead on

  • Maintenance & community, 56 against 35

Watch for

No API, MCP server or A2A endpoint, and the terms forbid unauthorised automated connections.

Score by category

CategoryWeight this runfolkZapiaEdge
Reliability16%202515folk +10
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.2100folk +10
Agent ergonomics13%16.200even
Security & auth14%17.533even
Payments & pricing10%12.53030even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.83556Zapia +21
Transparency & trust7%8.86759folk +8
Negative events≤1500
TotalF 19.8/100F 17.3/100

Facts side by side

FactfolkZapia
KindPlatformPlatform
VendorNozomio, Inc.Brainlogic AI S.A.S.
Hosted endpointno (local only)no (local only)
Transports
AuthOAuth or keyOAuth or key
PricingFreemiumFreemium
x402nono
LicenceClosed service under folk's terms (Nozomio, Inc., last updated 8 September 2026).Closed service under Zapia's terms and conditions (Brainlogic AI SAS). No date was found on the terms.
Read-only variant documentedyesno
llms.txtyesno
Last release2026-10-122026-10-12
Terms last updated
Privacy policy last updated
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver

Verdicts

folk

folk is a texting accountability assistant with its own cloud computer, a free tier with no card and a privacy policy that rules out training on user data. No outside agent can call it, no status page or changelog was found, and its llms.txt and pricing file disagree on free limits and Max's price.

Zapia

Zapia is a personal assistant app for iOS, Android and the web with a free plan that isn't a trial, weekly iOS releases and explicit confirmation before anything that writes. No outside agent can call it, and no status page, security contact or hosting location was found.

Before you call either

folk

  1. Don't plan to call folk from another agent. It answers only the owner's iMessage, Telegram or WhatsApp thread and iPhone app.
  2. Quote limits and prices from pricing.md and the terms, and note that llms.txt gives different free-tier limits.

Zapia

  1. Don't plan to call Zapia from another agent. It answers only in its own app and on the web, and the terms forbid unauthorised automated access.
  2. Tell the owner that Pro and Ultra trials become paid after 7 days unless cancelled in the app store.

Questions

Which is better for AI agents, folk or Zapia?

folk scores 19.8 (F) on agent readiness against Zapia's 17.3 (F), and leads in 3 of 7 scored categories. Zapia leads on maintenance & community.

Other comparisons with folk or Zapia

Disclosure

folk competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.

Zapia competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.

Machine-readable

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