Orca by Stably

Orca, built by Stably, is a free and open-source (MIT) Agent Development Environment (ADE) — a desktop app for macOS, Windows, and Linux that orchestrates multiple CLI coding agents in parallel. It works with virtually any terminal-based agent, including Claude Code, Codex, Cursor CLI, Grok, GitHub Copilot, OpenCode, Amp, Gemini/Antigravity, Devin, Goose, Cline, and many more, running each in its own isolated git worktree so you can fan one prompt across several agents, compare their results, and merge the winner. Beyond orchestration, Orca packs a full development workspace: Ghostty-class WebGL terminals with infinite splits and persistent scrollback, a VS Code-style editor with autosave and drag-and-drop of files or images into agent prompts, an embedded Chromium browser with a 'Design Mode' that sends any clicked element's HTML, CSS, and a cropped screenshot into the agent, native GitHub and Linear integration for browsing PRs, issues, and boards, AI-diff annotation for line-level review comments, and SSH worktrees to run agents on a remote box with auto-reconnect and port forwarding. A mobile companion app for iOS and Android lets you monitor and steer agents and get notified when they finish. The Orca CLI (orca worktree create, snapshot, click, fill) lets agents themselves script Orca workflows, and Computer Use lets agents operate desktop apps. Account switching tracks Claude and Codex usage and rate-limit resets. Installation is via direct download, Homebrew, or AUR; the team ships new features daily. Best for power users and teams running many AI coding agents at once. No MCP server/client is documented; integration is through the supported CLI agents.

What Orca by Stably does in practice

Run any CLI coding agent (Claude Code, Codex, Cursor, Copilot, and more) side-by-side. Isolated git worktrees per agent with fan-out prompts and merge-the-winner workflow. WebGL terminal splits, VS Code-style editor, and embedded Chromium browser with Design Mode. Native GitHub and Linear task workflows plus AI-diff annotation. SSH worktrees for remote execution and a mobile companion app to steer agents. Orca CLI and Computer Use so agents can script workflows and drive desktop apps. These capabilities should be judged as a workflow, not isolated checkboxes. Concrete best-fit scenarios include: Running multiple AI coding agents in parallel and comparing outputs. Isolating agent work in per-task git worktrees to avoid conflicts. Reviewing and annotating AI-generated diffs before merging. Steering long-running agents remotely from a phone or over SSH.

Pricing and total cost

Open Source is listed at Free; MIT-licensed desktop app for macOS, Windows, and Linux; Parallel CLI agents in isolated git worktrees; Ghostty-class terminals, VS Code editor, embedded browser; GitHub and Linear integration, SSH worktrees; iOS and Android mobile companion apps. These figures come from the staging record and vendor pages were fetched during this revision, but pricing is mutable. Recheck the live pricing page, checkout, or quote before publishing or buying. Include required seats, usage credits, model or API charges, storage, implementation, support, taxes, and overages. A free tier can prove compatibility; it does not necessarily model production volume or support needs.

Strengths and tradeoffs

Practical strengths include: Run any CLI coding agent (Claude Code, Codex, Cursor, Copilot, and more) side-by-side. This directly supports the primary workflow. Isolated git worktrees per agent with fan-out prompts and merge-the-winner workflow. WebGL terminal splits, VS Code-style editor, and embedded Chromium browser with Design Mode. Native GitHub and Linear task workflows plus AI-diff annotation. Important constraints include: Plan prices can omit usage overages, model-provider charges, implementation work, taxes, or required seats. Features and limits can change by plan, billing interval, region, and release; verify current documentation. No native MCP support is documented in this record, so agent workflows may require API work or another integration layer. A representative pilot is necessary because a feature list does not establish reliability or operational fit. A capable product can still be the wrong operational choice. Document who reviews outputs, who owns failures, what data may enter the system, and how work continues during an outage.

How to evaluate Orca by Stably

Use a two-week proof of concept on a noncritical repository or workflow. Define four repeatable tasks: one small change, one multi-file change, one failure-recovery scenario, and one permissions-sensitive action. Measure setup time, completion rate, human review minutes, retries, API or token cost, and defects found after acceptance. Pin versions, inspect generated changes, and test rollback. Give the tool only the repositories, APIs, and scopes needed for the trial. Verify export, backup, logging, rate-limit, and offboarding behavior before treating a fast demo as production readiness.

Bottom line

Orca by Stably is worth shortlisting when its capabilities match a measured workflow and the buyer can verify current limits. Do not select it from a feature list alone. Compare at least two alternatives using the same inputs, acceptance criteria, and cost model. Preserve test results so renewal is based on outcomes rather than adoption anecdotes.