Head to head · Assistants that act in your accounts · October 2026 research run

folk vs Tomo

folk scores 19.8 (F) on agent readiness against Tomo's 13.1 (F), and leads in 5 of 7 scored categories. Tomo leads on maintenance & community. Both do assistants that act in your accounts.

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
  • Payments & pricing, 30 against 10
  • Transparency & trust, 67 against 47

Also in its favour

  • Free to start without a card

Watch for

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

Tomo F

Best for People who want accountability, check-ins and reminders by text, including in group chats.

Ahead on

  • Maintenance & community, 54 against 35

Watch for

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

Score by category

CategoryWeight this runfolkTomoEdge
Reliability16%202515folk +10
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.2100folk +10
Agent ergonomics13%16.200even
Security & auth14%17.530folk +3
Payments & pricing10%12.53010folk +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.83554Tomo +19
Transparency & trust7%8.86747folk +20
Negative events≤1500
TotalF 19.8/100F 13.1/100

Facts side by side

FactfolkTomo
KindPlatformPlatform
VendorNozomio, Inc.Mapo Labs, Inc.
Hosted endpointno (local only)no (local only)
Transports
AuthOAuth or keyOAuth or key
PricingFreemiumPaid
x402nono
LicenceClosed service under folk's terms (Nozomio, Inc., last updated 8 September 2026).Closed service under Tomo's Terms of Service (Mapo Labs, Inc., last updated 24 September 2026).
Read-only variant documentedyesno
llms.txtyesno
Last release2026-10-122026-09-29
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.

Tomo

Tomo is a hosted personal AI over iMessage and an iOS app, centred on goals, check-ins and reminders, with regular iOS releases since April 2026. No outside agent can call it, there's no free plan, and no status page, security contact or subprocessor list 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.

Tomo

  1. Don't plan to call Tomo from another agent. It answers only the owner's iMessage, group chats it's added to and the iOS app.
  2. Tell the owner that adding Tomo to a group chat applies its terms to every participant.

Questions

Which is better for AI agents, folk or Tomo?

folk scores 19.8 (F) on agent readiness against Tomo's 13.1 (F), and leads in 5 of 7 scored categories. Tomo leads on maintenance & community.

Other comparisons with folk or Tomo

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.

Tomo 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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