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License: MIT Any use incl. commercial
Local-run terms: MIT license permits commercial use, modification, and distribution with attribution. Users may run Orbit locally, modify it, build commercial products on top, and redistribute derivative works under MIT terms.

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WinkTerm

FreeOpen SourceSelf-Hosted

Pricing

Model
Free

Summary

You shipped an agent, it passed the demo, and then you had no idea what it actually changed, why it passed, or whether you could reproduce the result — Orbit is the harness built for that exact gap.

Orbit wraps each coding-agent run in a bounded loop: one task selected from a dependency-ordered backlog, executed by whatever CLI agent you hand it, then validated through tests, lint, and type checks before the orbit closes. Every run writes structured JSON artifacts — what the agent returned, how the diff scored, whether the reviewer should accept or iterate. This is not an agent itself; it is the scaffold that keeps agents accountable. The ceiling appears when your workflow needs dynamic replanning or multi-agent coordination across parallel tasks — Orbit's contract is deliberately single-focus, and teams that outgrow that boundary are maintaining a layer above the harness.

Bottom line: Orbit earns its place when you need auditable, repeatable evidence that an agent actually fixed something — but if your workflow requires agents renegotiating scope mid-run or branching across parallel workstreams, you will be writing orchestration logic Orbit does not provide.

Community Performance Report Card

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Best For: Teams evaluating multiple AI coding agents, Projects requiring deterministic, auditable agent workflows, Self-hosted setups prioritizing local control and replaying, Developers building harness infrastructure for AI coding, Organizations standardizing validation gates for agent work

Community Benchmarks Community

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  • Validation gates (tests, lint, type checks) block an orbit from closing until the agent proves the work passed, so you stop shipping diffs that look correct but break the suite.
  • Four structured artifact files per run — agent result, evaluation, reviewer recommendation, progress log — so you have a durable, inspectable record of what the agent did and how it scored, instead of a conversation history you cannot query.
  • Agent-neutral JSON contract means you can run the same task through Claude, Codex, or Cursor and compare scored evaluation artifacts side by side, so agent selection becomes evidence-based rather than demo-based.
  • Dependency-aware backlog selection keeps each orbit focused on one task at a time, so the agent cannot drift scope mid-run and the validation result is unambiguous.
  • Fully self-hosted with no external API dependency for the core harness, so teams with data-residency requirements or air-gapped environments can run validated agent workflows without routing artifacts through a third-party service.
  • Orbit's contract is single-task and bounded by design — the moment a coding task cannot be expressed as one verifiable unit with a clear pass/fail validation suite, the orbit structure breaks down and teams are left writing wrapper logic that effectively duplicates Orbit's job at a higher level.
  • There is no built-in parallel execution or multi-agent coordination: teams that need agents working on interdependent tasks simultaneously hit the single-orbit model's ceiling and move to a purpose-built orchestration layer, at which point Orbit either becomes a sub-component or gets replaced entirely.
  • The adapter ecosystem depends on community contributions — the docs explicitly frame adapter development as a contributor responsibility, not a vendor roadmap item. Teams that need a production-grade adapter for a specific agent and cannot write it themselves are blocked until someone else builds and maintains it.

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About

Platforms
Linux, macOS, Windows (via Python)
API Available
No
Self-Hosted
Yes
Last Updated
2026-06-08T22:23:10.931Z

Best For

Who it's for

  • Teams evaluating multiple AI coding agents
  • Projects requiring deterministic, auditable agent workflows
  • Self-hosted setups prioritizing local control and replaying
  • Developers building harness infrastructure for AI coding
  • Organizations standardizing validation gates for agent work

What it does well

  • Validating AI agent work before merging to main
  • Running batches of bounded coding tasks with structured evidence
  • Comparing different coding agents side-by-side using the same task contract
  • Self-healing repositories that auto-detect and remediate failures
  • Audit trails and progress tracking for agentic development workflows

Integrations

ClaudeCodexCursorany JSON-speaking CLI; gittestslinttype checkers

Discussion Community

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Community Notes & Tips Community

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Frequently Asked Questions

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

Hours Saved & ROI Stories Community

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WinkTerm

Most coding-agent workflows produce a diff and a thumbs-up from the model itself — no structured proof, no replay, no way to compare one agent against another on the same task. Orbit changes the contract: it selects a task from a dependency-aware backlog, hands it to a JSON-speaking CLI agent, runs the validation suite (tests, lint, type checks), and only closes the orbit if the agent can prove the work passed. The run leaves four durable artifacts — a structured agent result, a rubric-scored evaluation, a reviewer recommendation, and a human-readable progress log — so the evidence outlasts the session.

The differentiating feature is agent neutrality behind a fixed contract. The vendor states Orbit works with Claude, Codex, Cursor, or any agent that speaks JSON from the CLI. That means you can run the same task through two different agents and compare evaluation artifacts instead of comparing anecdotes. Adapter experiments become a controlled test rather than a gut feeling, which matters when your organization is deciding which agent toolchain to standardize on.

Orbit fits teams in the messy middle: validation gates for self-healing repositories where failing tests need proof of remediation before a task closes, backlog execution for projects that need dependency-ordered progress without human shepherding every step, and audit trails for organizations that need to show what changed and why. Where it breaks: the bounded single-task model is the design, not a limitation to route around. Teams that need agents to replan mid-orbit, coordinate across parallel workstreams, or handle tasks that cannot be scoped to a single verifiable unit will find Orbit’s contract too rigid and will need orchestration infrastructure above it.

Orbit is MIT-licensed and self-hosted by design — no API key is required to run the deterministic replay demo. The project is intentionally scoped: the docs describe contributions as things that make the harness easier to verify, replay, or connect to another agent workflow, signaling that scope expansion is a community concern, not a roadmap guarantee.