Gotcha vs LoopX: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Gotcha and LoopX — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
G
Gotcha
Samosa-AI
On-device Android AI copilot that turns natural language into 100+ real device actions with dual safety modes.
Key features
- On-device Copilot: Runs entirely on the user's Android phone with a bring-your-own-model architecture, so prompts, screen context, and actions never require a cloud round-trip.
- Dual Copilot Modes: Instant switch between Monitor mode (40+ read-only tools for planning) and Operator mode (all 100+ tools for execution) so users choose inspection vs action per task.
- 100+ Native Device Tools: Send SMS, place calls, manage storage, toggle torch, set wallpapers, read the screen, control volume, and automate any app through a curated Android tool library.
- Tiered Permission Model: Four permission tiers (from everyday battery/storage access to Tier 4 privileged root actions) with explicit gates so nothing runs without user consent.
- Assistive Ball & Push-to-Talk: A floating orb accessible from any app supports 'Hey Gotcha' voice calls where Gotcha sees the current screen and acts on the user's behalf.
- Visual Safety Indicators: A colored ring around the screen shows when Gotcha is reading the display (blue) or working in the background (orange), backed by an append-only audit log.
- BYOK Model Choice: Bring your own local or cloud model — or use free Samosa AI credits routed through the OpenAI-compatible Samosa AIR API for zero setup.
- Skill Hub Extensibility: Add third-party skills through the Gotcha Skill Hub so the copilot's action library grows with the community.
Best for
- Hands-free Messaging: Say 'Text mom I'm running late' and Gotcha finds the contact and sends the SMS without touching the screen.
- Storage Cleanup: Ask Gotcha to free up space and it inspects app usage, then confirms uninstalls of games or apps you haven't opened in months.
- Screen-aware Shopping: While browsing a page, ask 'find similar shirts to the one worn here' and Gotcha reads the screen, searches the web, and returns matches.
- Quick Device Control: Toggle the torch, set the volume, change wallpaper, or open a specific playlist in Spotify with one spoken sentence.
- Privacy-first Automation: Users who don't want cloud LLMs running their phones can plug in a local model and keep prompts, screen content, and actions on-device.
- Developer Copilot Extensions: Ship a Gotcha Skill so a niche workflow (e.g., custom-app automation) becomes a first-class action inside the copilot.
L
LoopX
huangruiteng
Provider-neutral state kernel and local-first control plane for governing long-running AI agent loops across Codex, Claude Code, Cursor, and peer teams.
Key features
- Loop-Engineering State Kernel: A compact durable-state layer that keeps objectives, gates, todos, evidence, quotas, and handoffs consistent across many bounded turns.
- Runtime-Agnostic: Governs work executed by any coding-agent runtime — Codex, Claude Code, Cursor, or your own — without replacing them.
- Peer-Agent Model: Registered agents are peers; claims, leases, capabilities, and typed continuation decide who acts next, with no durable leader identity.
- Kanban-Style Control Plane: Cards carry identity, authority, evidence, and continuation; moves are validated operators (claim, gate, monitor, writeback).
- Local-First: The control plane runs locally by default — the public/private boundary is explicit, so private data and code stay on your machine.
- Auto-Wake and Quotas: Quota-aware auto-wake keeps agents progressing on long-running goals without a runaway scheduler.
- Evidence & Continuation: 200+ hour example loops preserve decision lineage, evidence branches, and invalid experiments across turns.
- Human-In-Command: Dangerous permissions, publishing, and production writes remain gated to the human owner — not autonomous.
Best for
- Multi-Day SWE Loops: Drive week-long engineering objectives across many bounded agent turns while keeping scope and review state intact.
- PR/Issue Automation: Preserve review state, evidence, and reviewer preferences across a PR that touches multiple turns and agents.
- Auto-ML Experiments: Keep hypotheses, matched evidence, invalid lineages, and promote/stop gates visible in a single graph over hundreds of hours.
- Multi-Agent Coordination: Coordinate a peer team of Codex + Claude Code + Cursor agents on the same objective with typed handoffs.
- Recurring Monitors: Run heartbeat or monitoring loops with owner-visible gates and evidence trails.
- Creator/Research Workflows: Give non-engineering owners a legible board of long-running work with human sign-off at each gate.