Clixad
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AgentKitten
FreeOpen SourceSelf-HostedAgentic
Agent runs that look successful in the demo log but leave no trail of what actually changed, what validation ran, or whether the diff matched the task — that's the problem Orbit was built to close. It wraps any JSON-speaking coding agent in a harness that enforces validation gates, records structured artifacts per run, and refuses to mark work complete until the agent can prove it.
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Get This Tool
License: MIT Any use incl. commercial
Local-run terms: MIT license permits free use, modification, and distribution in commercial and private projects, provided the license notice and copyright are retained.
Pricing
- Model
- Free
Summary
Orbit selects a task from a dependency-ordered backlog, hands it to the configured agent adapter, runs tests, lint, and type checks against the result, and only advances the orbit when those gates pass. Every run writes four artifacts: structured agent output, rubric scoring, an accept-or-iterate recommendation, and a human-readable progress log. The workflow is agent-neutral — Claude, Codex, Cursor, or any adapter you wire up behind the same contract. Where it breaks: Orbit is intentionally minimal, so teams expecting a hosted dashboard, a GUI, or built-in multi-agent parallelism will find precious little of that. The harness is a loop, not a platform.
Bottom line: Orbit earns its place in a codebase where you need auditable proof that an agent actually fixed the failing test — not just that it returned a success code; it does not serve teams who need a managed cloud runtime or visual workflow editor.
Find your fit
Before you choose AgentKitten
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 block task advancement until tests, lint, and type checks pass, which means the agent cannot silently ship a broken diff and have it logged as complete.
- Four structured artifact files per orbit — result, evaluation, review, and progress log — so you can compare agent behavior across models with evidence instead of anecdotes.
- Dependency-ordered backlog execution keeps each orbit scoped to one task at a time, so the run log stays traceable and retries do not bleed context across unrelated work.
- Agent-neutral adapter design, so swapping the underlying coding model behind the same validation contract requires no changes to the harness or the artifact schema.
- MIT licensed and self-hosted with a replay demo that needs no API key, so you can audit the full workflow loop before committing any credentials or infrastructure.
Cons
- There is no API, no hosted runtime, and no GUI — all interaction is CLI-driven and all artifacts are local JSON files, so any team that needs a dashboard their product manager can open without a terminal will build that layer themselves or abandon Orbit for a platform that ships one.
- The harness runs one orbit at a time in a single-task loop; teams that need parallel agent execution across multiple workstreams hit this architectural boundary immediately and route around it by running separate harness instances manually, which breaks the unified progress trail.
- Adapter support covers JSON-speaking CLI agents, but integrating a coding tool that does not expose a CLI or JSON output requires writing and maintaining a custom adapter — at which point the integration work exceeds what smaller teams budgeted for a validation harness.
- The artifact schema and rubric scoring are defined by the harness; teams with compliance requirements that specify a different evidence format reformat the JSON downstream or switch to a purpose-built audit pipeline that natively matches their schema.
About
- Platforms
- Linux, macOS, Python 3.8+
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- June 6, 2026
Best For
Who it's for
- Teams building or testing AI coding agents and needing deterministic validation
- Projects requiring auditable, reproducible agent-driven workflows
- Developers comparing agent behavior across different models or adapters
- Orgs that need evidence and progress logs for compliance or review
- Rapid experimentation with agent harness designs
What it does well
- Self-healing repositories by failing tests and requiring agent proof before task completion
- Backlog execution with dependency-ordered task sequences and verified advancement
- Comparing coding agents behind the same validation contract
- Creating deterministic, auditable records of agent-driven development
- Breaking work into bounded, validated orbits with retry capability
Integrations
Frequently Asked Questions
- Is AgentKitten free?
- AgentKitten offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is AgentKitten open source?
- Yes. AgentKitten is open source.
- Can I self-host AgentKitten?
- Yes. AgentKitten supports self-hosting on your own infrastructure.
- What platforms does AgentKitten support?
- AgentKitten is available on: Linux, macOS, Python 3.8+.
Curated lists that include this category
AI coding agents produce output that is easy to accept and hard to verify. Orbit addresses this by wrapping each unit of agent work — one task, one orbit — in a deterministic loop: backlog selection, agent execution, validation via tests and lint, artifact recording, and a scored recommendation to accept, iterate, or stop. The harness drives the loop itself, advancing work only when validation gates close. A replay demo ships with the repo and runs without an API key, so you can inspect the full artifact chain before connecting a real agent.
The core differentiator is the artifact contract. Every orbit produces four structured files: agent-result.json captures what the agent returned and which files changed; evaluation.json scores the run against a rubric covering task focus, completion signal, and diff quality; review.json emits an accept-or-iterate decision; and progress.md gives a human-readable record reviewers or compliance logs can consume. This makes agent behavior comparable across runs and across models — you are reading structured evidence, not scrolling terminal output.
Orbit fits tightest in two scenarios: self-healing repositories, where you feed the harness failing tests and require the agent to produce a green run before the task closes; and adapter experimentation, where you swap coding agents behind the same validation contract and compare artifacts instead of gut feelings. Where it strains: the harness is minimal by design. There is no hosted runtime, no GUI, no built-in support for parallel agent execution, and no API surface. Teams who outgrow CLI-driven single-task loops — or who need a product their non-engineering stakeholders can observe without reading JSON — will hit that ceiling and look elsewhere.
Orbit is MIT licensed, self-hosted, and free with no paid tier. The vendor describes it as intentionally small, and the contribution guidance explicitly names making the harness easier to verify, replay, or connect to other workflows as the priority — not expanding its feature surface. Integration requires an agent adapter that speaks JSON over CLI; the docs cite Claude, Codex, and Cursor as supported examples.
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