Agent workspaces; Parallel coding; Worktrees
Orca is an agent-development environment for working with multiple command-line coding agents. Each task can run in an isolated worktree, with terminals, diffs and a browser available in the same application. The official site lists support for agents such as Claude Code and Codex while allowing developers to use the subscriptions they already have. A mobile companion can show live agent status and provide terminal access away from the desktop. Its App Store listing is published by Lovecast LLC and links to onorca.dev. Orca's purpose is coordinating development sessions and review, not supplying the underlying model itself.
Orca Agent Development Environment is best described as AI Agent Development Tool for developers, engineering teams. The practical workflow centers on parallel coding agents, isolated worktrees, integrated terminals, code diffs, mobile terminal companion. Users normally bring instructions, code context into the product and review code before relying on it.
Useful use cases include agent workspaces, parallel coding, worktrees. Category placement is kept to AI Agent Development because the tool should be listed where people would actually compare it. Supported access is recorded as iOS, macOS, and integrations are limited to connections that could be verified from the available source material.
The developer is recorded as Stably AI (Orca). Pricing is listed conservatively as Current pricing should be checked on the official site. Free-plan status is recorded as Free access terms are not clearly established in the reviewed public material. Before using Orca Agent Development Environment for production work, check the current plan page, account limits and any commercial-use terms that apply to the files, data, media or decisions involved.
Run a small real task first and compare the result with the original material. For generated text, media, code, analysis or operational actions, review factual claims, permissions and handoff steps before publishing or applying the output. This keeps the listing useful without adding unsupported benchmarks, invented model names or broad legal promises.
