Gito

Orbit calls itself mission control for AI coding agents, and the framing is accurate: it does not generate code itself. It runs a loop — select a task from a dependency-ordered backlog, hand the task to whichever agent adapter is configured, wait for the agent to return a result, run validation gates (tests, lint, type checks), and record structured artifacts before deciding whether the orbit closes or iterates. Every run produces four files: a structured agent-result JSON, an evaluation JSON with rubric scoring across task focus and diff signal, a review JSON with an accept-or-iterate recommendation, and a human-readable progress log. Nothing advances unless the validation gates pass.

The key differentiating feature is agent neutrality enforced through a common contract. Because Orbit communicates with agents over a JSON-speaking CLI interface, swapping Claude for Codex for Cursor is an adapter change, not an architecture change. The vendor page describes this explicitly as a mechanism for comparing agents against the same artifacts rather than comparing anecdotes — which means teams evaluating multiple agent frameworks get an apples-to-apples record of what each one actually produced and whether it passed the gate.

Orbit fits tightly in two scenarios: self-healing repository workflows where failing tests or lint issues need proof of resolution before a task is marked done, and backlog execution pipelines where dependency order matters and you want one verified step to gate the next. It breaks at the edges of those scenarios. The project is described by the vendor as ‘intentionally small,’ with no hosted option, no API, and no GUI — teams that need cross-team visibility into agent runs or integration with a broader developer portal will wire their own layer on top. That layer becomes a maintenance surface. At the point where a team is building more scaffolding than using the harness, the case for a purpose-built CI-native agent framework becomes easier to make.

The deterministic replay demo runs without an API key, using a mock path to show task selection, agent execution, validation, and artifact recording. The vendor states the project is MIT licensed and explicitly invites contributions in three categories: adapters for new agents, new demo missions, and mission templates — framing contribution around verifiability and replay fidelity rather than feature surface area.