LoopX vs Orca: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of LoopX and Orca — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
O
Orca
Stably AI
Desktop AI orchestrator that runs Codex, Claude Code, OpenCode, and Pi side by side in parallel git worktrees, all tracked in one place.
Key features
- Parallel Agent Worktrees: Fan one prompt across up to five agents, each in its own isolated git worktree — compare results and merge the winner.
- Multi-Agent Support: Run Codex, Claude Code, OpenCode, and Pi side by side, all tracked in one unified interface.
- Mobile Companion App: Monitor and steer your agents from iOS or Android — get notified when an agent finishes and send follow-ups from anywhere.
- Ghostty-Class Terminals: WebGL-rendered terminal splits with infinite panes and scrollback that survives restarts.
- Design Mode Browser: Click any UI element in the embedded Chromium window to send its HTML, CSS, and a cropped screenshot straight into your agent's prompt.
- Cross-Platform Desktop: Native builds for macOS, Windows, and Linux so the orchestrator runs alongside your existing dev environment.
- Unified Prompt & History Tracking: Every prompt, tool call, and terminal action across all agents is captured in one place for easy review.
- Steer from Anywhere: Follow-up prompts from the mobile companion keep long-running agent runs moving even when you step away from the desk.
Best for
- Prompt Bake-Off: Send the same feature request to Codex, Claude Code, and OpenCode simultaneously and merge whichever branch wins.
- Long-Running Refactors: Kick off multi-hour agent runs in isolated worktrees and check in from your phone as they progress.
- Design-to-Code Handoff: Click a live UI element in Design Mode and hand its markup and a screenshot to the agent for pixel-accurate implementation.
- Parallel Bug Reproduction: Try multiple diagnostic approaches at once — each agent operates on its own worktree without collision.
- Terminal-Heavy Workflows: Use Ghostty-class terminal splits to keep build watchers, servers, and agent output visible side by side.
- Team Handoffs: Track every agent action in one place so the next engineer can pick up context without replaying a chat log.