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

folk vs Orbits

folk scores 19.8 (F) on agent readiness against Orbits's 15.6 (F), and leads in 4 of 7 scored categories. Orbits 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
  • Payments & pricing, 30 against 20
  • 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.

Orbits F

Best for Families who want one shared assistant for calendars, lists, meals, chores and household email.

Ahead on

  • Maintenance & community, 54 against 35

Watch for

No public agent API, as llms.txt states.

Score by category

CategoryWeight this runfolkOrbitsEdge
Reliability16%202515folk +10
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.21010even
Agent ergonomics13%16.200even
Security & auth14%17.530folk +3
Payments & pricing10%12.53020folk +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.83554Orbits +19
Transparency & trust7%8.86743folk +24
Negative events≤1500
TotalF 19.8/100F 15.6/100

Facts side by side

FactfolkOrbits
KindPlatformPlatform
VendorNozomio, Inc.Nemus AI, 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 the Orbits Terms of Service (Nemus AI, Inc.).
Read-only variant documentedyesno
llms.txtyesyes
Last release2026-10-122026-10-13
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.

Orbits

Orbits is a family household app whose assistant, Bit, runs shared calendars, lists and meals, reads household email and places restaurant calls after approval, with iOS releases every week or two. No outside agent can call it, prices appear only as App Store in-app purchases, and no status page, security contact or named subprocessors were 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.

Orbits

  1. Don't plan to call Orbits from another agent. llms.txt says no public agent API is offered.
  2. Tell the owner the assistant needs an Orbits subscription bought in the app, and that any trial appears only at purchase.

Questions

Which is better for AI agents, folk or Orbits?

folk scores 19.8 (F) on agent readiness against Orbits's 15.6 (F), and leads in 4 of 7 scored categories. Orbits leads on maintenance & community.

Other comparisons with folk or Orbits

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.

Orbits 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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