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License: MIT Any use incl. commercial
Local-run terms: MIT license permits commercial and private use of the software with attribution. Full source code is publicly available; anyone can clone, modify, and run locally without vendor involvement.

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Supertonic

FreeOpen SourceSelf-HostedAgentic

Pricing

Model
Free

Summary

Agent runs that produce no audit trail are impossible to debug, impossible to trust, and impossible to hand to a reviewer who needs proof — not vibes. Orbit wraps each coding agent task in a validation loop that does not close until the work can be proven.

Orbit structures agent execution around a single concept: one task, one orbit, bounded by real checks — tests, lint, type validation — and recorded in inspectable JSON artifacts before anything advances. The vendor describes it as agent-neutral: Claude, Codex, Cursor, or any JSON-speaking CLI slots in behind the same contract, so teams can swap agents and compare output artifacts instead of gut feelings. The architecture is intentionally small, which means the harness is easy to verify and replay, but it also means Orbit does not ship workflow UI, cloud hosting, or a managed backlog service. Teams with complex multi-agent pipelines or a need for a hosted dashboard will be assembling those pieces themselves. Where it shines is the messy middle: failing tests handed to an agent, with proof required before the task closes.

Bottom line: Orbit is the right harness for a team that needs bounded, auditable agent execution on a local or self-hosted stack — and the wrong choice the moment you need a hosted dashboard, a managed backlog, or anything that does not speak JSON from the command line.

Community Performance Report Card

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Best For: Teams building reliable AI coding workflows with proof requirements, Organizations evaluating or swapping between multiple coding agents, Projects requiring deterministic replay and artifact inspection, Development teams that need bounded, auditable agent execution, Researchers experimenting with different agentic patterns

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  • Validation gates block task completion until tests, lint, and type checks pass — so an agent cannot mark work done without machine-verifiable proof, eliminating the silent failure mode where agents self-report success on broken code.
  • Agent-neutral JSON contract means swapping from one coding agent to another requires no structural changes to the harness, so teams evaluating multiple agents get comparable artifact sets instead of incomparable anecdotes.
  • Dependency-aware backlog selection keeps each orbit scoped to one task at a time, which means multi-step projects advance in verified increments rather than accumulating unvalidated drift across parallel agent threads.
  • Structured artifact output — agent-result.json, evaluation.json, review.json, progress.md — gives reviewers an inspectable record of every decision and validation outcome, so audits and post-mortems have a durable evidence trail instead of reconstructed logs.
  • MIT-licensed and self-hosted, so the entire execution environment stays on infrastructure the team controls — no vendor dependency on a cloud service that changes pricing or availability.
  • Orbit ships no hosted control plane, no web UI, and no managed backlog service — teams that need non-engineers to review agent progress or manage task queues through a dashboard face a full custom build before Orbit is usable at that level.
  • The harness requires every coding agent to speak JSON from the CLI; agents or tools that expose only browser-based or proprietary interfaces cannot be connected without writing and maintaining a custom adapter, which adds an ongoing maintenance surface.
  • At the scale where a team needs parallel agent execution across multiple repositories with centralized visibility, Orbit's intentionally small scope becomes a structural ceiling — teams at that point are evaluating purpose-built CI orchestration platforms or managed agent infrastructure, not a local harness.

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About

Platforms
Python; Linux, macOS, Windows (via WSL or native Python)
API Available
No
Self-Hosted
Yes
Last Updated
2026-06-01T13:36:17.616Z

Best For

Who it's for

  • Teams building reliable AI coding workflows with proof requirements
  • Organizations evaluating or swapping between multiple coding agents
  • Projects requiring deterministic replay and artifact inspection
  • Development teams that need bounded, auditable agent execution
  • Researchers experimenting with different agentic patterns

What it does well

  • Self-healing repositories with automated test and lint fixing
  • Backlog-driven multi-task execution with dependency ordering
  • Comparing different coding agents empirically via artifact inspection
  • Wrapping agent execution in validation gates for production readiness
  • Generating durable, auditable logs of agent decisions and validation outcomes

Integrations

ClaudeCodexCursorany JSON-speaking CLI; GitHub Actions compatible

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Frequently Asked Questions

Is Supertonic free?
Yes — Supertonic is fully free to use. There is no paid tier.
Is Supertonic open source?
Yes. Supertonic is open source.
Can I self-host Supertonic?
Yes. Supertonic supports self-hosting on your own infrastructure.
What platforms does Supertonic support?
Supertonic is available on: Python; Linux, macOS, Windows (via WSL or native Python).

Hours Saved & ROI Stories Community

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Supertonic

Orbit is a free, MIT-licensed open-source harness for running AI coding agents in bounded, validated, auditable loops. The core workflow is task-scoped: Orbit selects one task from a dependency-ordered backlog, hands it to a configured coding agent, runs validation gates — tests, lint, type checks — and decides whether the orbit closes or retries. Every run produces a structured artifact set: agent-result.json capturing what the agent returned and which files changed, evaluation.json scoring the run against a rubric for task focus and diff signal, review.json recommending accept, iterate, or stop, and a human-readable progress.md log. The deterministic replay demo runs without an API key, letting teams inspect the full loop before connecting a live agent.

The differentiating feature is the artifact-based contract. Because every agent sits behind the same JSON-speaking interface, teams can swap coding agents — Claude for Codex, or a custom CLI adapter — and compare runs by inspecting the same artifact schema rather than re-running qualitative evaluations. The vendor frames this explicitly as a tool for empirical agent comparison, which is rare: most agent harnesses are built to run one agent well, not to make agents substitutable.

Orbit fits teams that need traceable, repeatable workflows and are comfortable operating from the command line on a self-hosted stack. The harness is intentionally small by design — the vendor states that the best contributions make it easier to verify, replay, or connect to another workflow — which means there is no managed cloud service, no visual workflow builder, and no built-in backlog management beyond dependency-ordered task selection. Teams that need a hosted control plane, a GUI for non-technical reviewers, or integrations beyond JSON CLI adapters will be building scaffolding that Orbit does not provide.