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

asaply vs folk

folk scores 19.8 (F) on agent readiness against asaply's 7.6 (F), and leads in 5 of 7 scored categories. Both do assistants reached by message.

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

Which one, for what

asaply F

Best for People in covered neighbourhoods who want coffee, lunch, tickets and rides handled by text.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

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

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 10
  • Payments & pricing, 30 against 0
  • Maintenance & community, 35 against 2
  • Transparency & trust, 67 against 43

Also in its favour

  • Free to start without a card

Watch for

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

Score by category

CategoryWeight this runasaplyfolkEdge
Reliability16%201025folk +15
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.21010even
Agent ergonomics13%16.200even
Security & auth14%17.503folk +3
Payments & pricing10%12.5030folk +30
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.8235folk +33
Transparency & trust7%8.84367folk +24
Negative events≤1500
TotalF 7.6/100F 19.8/100

Facts side by side

Factasaplyfolk
KindPlatformPlatform
VendorAsaply Inc.Nozomio, Inc.
Hosted endpointno (local only)no (local only)
Transports
AuthOAuth or keyOAuth or key
PricingPay per useFreemium
x402nono
LicenceClosed service under asaply's Terms of Service, Privacy Policy and Messaging Terms (Asaply Inc., a Delaware corporation, last updated 11 October 2026).Closed service under folk's terms (Nozomio, Inc., last updated 8 September 2026).
Read-only variant documentednoyes
llms.txtyesyes
Last releasenone2026-10-12
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

asaply

asaply is a text-only errand assistant in early access that orders, books and pays through Apple Pay after showing the total, and can read and change a connected Google Calendar and Gmail. No outside agent can call it, the service fee isn't published, and no status page or security contact was found.

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.

Before you call either

asaply

  1. Don't plan to call asaply from another agent. It answers only texts from the owner's phone number and group chats it joins.
  2. Tell the owner each errand adds an unpublished service fee to the order total.

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.

Questions

Which is better for AI agents, asaply or folk?

folk scores 19.8 (F) on agent readiness against asaply's 7.6 (F), and leads in 5 of 7 scored categories.

Other comparisons with asaply or folk

Disclosure

asaply 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.

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.

Machine-readable

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