Clixad
Freemium
Developers who prefer terminal workflows
KugelAudio
FreeOpen SourceSelf-HostedAgentic
You ship an AI coding agent, it returns code that looks correct, tests fail, and you have no record of what the agent actually did or why the run scored the way it did. Orbit exists for exactly that gap.
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License: MIT Any use incl. commercial
Local-run terms: MIT license permits free use, modification, and commercial distribution with attribution. No restrictions on commercial or proprietary derivative works.
Pricing
- Model
- Free
Summary
Orbit wraps agent runs in a controlled loop: pick a task from a dependency-ordered backlog, hand it to whichever agent backend you have configured, run tests and lint against the output, and write inspectable JSON artifacts before the task is ever marked complete. If the agent cannot pass the validation gate, the orbit does not close — no silent failures, no optimistic merges. The artifact trail covers what the agent returned, how the run scored against a rubric, and a human-readable recommendation to accept, iterate, or stop. It runs fully self-hosted with no hosted option and no API key required for the replay demo.
Bottom line: Orbit is the right harness for teams who need proof that an agent's output actually passed tests before anyone reviews it — it is not a solution for teams who need a hosted orchestration service or want to skip writing the task definitions and validation config themselves.
Find your fit
Before you choose KugelAudio
More options in CLI Coding Agents. Compare pricing, platforms, and features side by side.
Airuncode
Freemium
Developers preferring fully local agent execution
Pros
- Validation gates enforce test, lint, and type-check passage before a task closes, which means agent-generated code that looks correct but breaks the build cannot silently advance through the backlog.
- Four structured artifacts per run — agent result, rubric evaluation, accept/iterate/stop recommendation, and a progress log — so teams can audit exactly what happened in any orbit without reconstructing it from logs.
- Agent-neutral adapter contract, so swapping from one coding agent backend to another is a configuration change rather than a workflow rebuild, and comparing two agents on identical tasks produces comparable JSON evidence.
- Dependency-ordered backlog execution keeps the harness from running tasks out of sequence, which means a task that depends on an earlier verified output cannot start until that upstream orbit has closed.
- MIT licensed and entirely self-hosted, so there is no usage ceiling, no data leaving the local environment, and no vendor dependency to manage.
Cons
- The self-healing loop only works if the repo already has meaningful test and lint coverage. Teams with sparse or absent tests get the artifact trail but lose the core validation mechanism — the harness has nothing to run against and cannot determine whether an orbit should close.
- Orbit has no hosted service, no visual interface, and no managed backlog. Teams that need a workflow builder, a dashboard, or a service they do not have to operate themselves will find the harness's intentionally small scope a hard limit — and those teams switch to a hosted orchestration platform rather than extend Orbit.
- There is no API surface exposed by Orbit itself. Integrating Orbit into a broader CI pipeline or triggering orbits from external systems requires wrapping the CLI directly, which adds integration work that grows with pipeline complexity.
About
- Platforms
- Linux, macOS, Windows
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- June 1, 2026
Best For
Who it's for
- Teams running AI coding agents at scale
- Projects requiring deterministic replay and auditability
- Developers experimenting with multiple agent backends
- Repos with strong test and lint requirements
- Organizations needing proof-of-work evidence trails
What it does well
- Self-healing repositories with automated test-driven task completion
- Comparing different AI coding agents (Claude, Codex, Cursor) on identical tasks
- Validating agent-generated code before human review
- Building reproducible and auditable agent workflows
- Dependency-ordered task execution with verification gates
Integrations
Compare KugelAudio
Frequently Asked Questions
- Is KugelAudio free?
- KugelAudio offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is KugelAudio open source?
- Yes. KugelAudio is open source.
- Can I self-host KugelAudio?
- Yes. KugelAudio supports self-hosting on your own infrastructure.
- What platforms does KugelAudio support?
- KugelAudio is available on: Linux, macOS, Windows.
Curated lists that include this category
Orbit functions as a harness around AI coding agents rather than an agent itself. The core workflow is a bounded loop: a dependency-aware backlog selects one task, the harness delegates execution to a configured agent (Claude, Codex, Cursor, or any CLI that speaks JSON), then runs tests, lint, and type checks against the result. Only a passing validation gate closes the orbit. Every run writes four artifacts — a structured agent result, a rubric-scored evaluation, an accept/iterate/stop recommendation, and a human-readable progress log — so nothing about the run is implicit.
The differentiating feature is agent-neutral adapter contracts. The vendor page describes the architecture as swapping coding agents behind the same interface and comparing artifacts instead of anecdotes. That means a team can run Claude and Codex against identical tasks, collect evaluation JSON from both runs, and make the comparison on evidence rather than gut feel. This is the capability that makes Orbit useful for teams evaluating which agent backend to standardize on.
Orbit fits repos that already have strong test and lint coverage — the validation gates require something to gate against. A repo with sparse tests gets the artifact trail but loses the self-healing loop, because there is nothing to prove completion. The tool is intentionally scoped: the vendor page describes it as ‘intentionally small,’ with contributions expected to make the harness easier to verify or replay rather than expand its surface area. Teams that need a hosted service, a visual workflow builder, or a managed backlog system will hit the ceiling of what a self-hosted Python harness provides and will look elsewhere.
Installation is a git clone and a pip install; the deterministic replay demo runs without an API key using a MOCK flag. MIT licensed, with no paid tier and no commercial hosted variant. Artifacts are JSON and Markdown files written to disk, making them readable by any downstream audit or CI process without proprietary tooling.
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