Coasty vs LoopX: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Coasty and LoopX — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Coasty
Coasty
Computer-use AI agent and API (85.60% OSWorld) that browses, clicks and types across real desktop apps to finish work end-to-end.
Key features
- Best-in-Class Computer Use: Ranked #1 on OSWorld at 85.60%, executing long-horizon tasks across real desktop applications.
- Predict API: `/v1/predict` accepts a screenshot and returns the next action as structured JSON at $0.05 per call.
- Always-On Virtual Machines: Persistent, always-on VMs let agents keep state between tasks and pick work up where they left off.
- Agent Swarms: Run multiple agents in parallel to fan out across steps or accounts, cutting wall-clock time on repetitive work.
- 1,000+ App Integrations: Ships with native integrations to common productivity apps like Gmail and Slack out of the box.
- Synthetic Trajectory Data: Delivers custom long-horizon multimodal training trajectories verified before delivery for enterprise buyers.
Best for
- Back-Office Automation: Insurance, accounting and freight teams delegate repetitive desktop work — data entry, form filling, portal ops — to agents.
- Autonomous Web Tasks: Consumers ask Coasty to book appointments, research options and complete purchases end-to-end.
- Developer Automation: Engineers embed the Predict API into their own harnesses to build custom agentic workflows.
- Healthcare Ops: Clinics use computer-use agents against legacy systems that lack modern APIs.
- Data Collection at Scale: Enterprises order synthetic multimodal trajectories tuned to their apps for model fine-tuning.
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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.