Cadenya
Freemium
Developers building agentic workflows
Conversations in AI Coding Agent
FreeOpen SourceSelf-HostedAgentic
AI coding agents produce output you cannot easily audit — a diff lands, tests pass locally, and three days later you find the agent skipped a constraint you never formally validated. Orbit exists to close that gap.
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License: MIT Any use incl. commercial
Local-run terms: MIT license permits free use, modification, and distribution in commercial projects with attribution. Source code is publicly available on GitHub; users can clone, modify, and run locally without vendor involvement.
Pricing
- Model
- Free
Summary
Orbit is an MIT-licensed, self-hosted harness that wraps a coding agent run in a bounded loop: it selects a task from a dependency-ordered backlog, hands off to whatever agent you plug in, runs tests and lint as a hard gate, and writes structured JSON artifacts that record exactly what happened. Every closed orbit leaves four files — agent output, rubric scoring, an accept-or-iterate recommendation, and a human-readable progress log. The demo runs without an API key, which means you can verify the mechanics before committing any credentials. The harness is agent-neutral by design; the vendor page cites Claude, Codex, and Cursor as examples. Where it shows its seams: Orbit is intentionally small, so teams needing a hosted dashboard, team-level access controls, or CI/CD pipeline integration will be writing that glue themselves.
Bottom line: Pick Orbit when you need auditable, repeatable evidence that an agent actually completed a task — not just that it returned output; hit a wall when your team needs a hosted review interface or organizational access controls, because neither exists yet.
Find your fit
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Brain Memory
Free · Open source
Long-term continuity in multi-session coding projects
Pros
- Dependency-aware backlog selection keeps each agent run scoped to one task at a time, so an agent cannot silently advance to dependent work before the current task passes validation.
- Validation gates — tests, lint, and type checks — must pass before an orbit closes, which means a task that looks complete but breaks the build cannot be marked done without explicit override.
- Structured artifact output (four consistent JSON and Markdown files per run) means comparing two different coding agents produces side-by-side evidence rather than impressions, so adapter selection becomes a reviewable decision.
- Agent-neutral adapter contract supports Claude, Codex, Cursor, or any JSON-speaking CLI, so swapping agents when one underperforms does not require restructuring the harness.
- MIT licensed with a public repository and a no-API-key demo, so teams can verify the full harness loop before committing credentials or infrastructure.
Cons
- No hosted dashboard or web UI exists — all artifact review happens by reading JSON and Markdown files directly, which becomes friction at the point when a non-engineering stakeholder needs to sign off on agent work at any meaningful volume.
- CI/CD pipeline integration is not provided out of the box; teams that want Orbit's validation gates to block a merge must write the pipeline glue themselves, adding a maintenance surface that grows with each new workflow.
- The project is explicitly described as 'intentionally small,' meaning teams that need role-based access controls, audit log retention policies, or enterprise compliance features will find none of that here — and will switch to a more opinionated platform rather than build it on top of Orbit.
About
- Platforms
- Cross-platform (Python)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- June 4, 2026
Best For
Who it's for
- Teams validating AI coding agent outputs
- Researchers comparing different coding agents
- Projects requiring audit trails of agent-generated code
- Local experimentation with agent harnesses
- Repositories needing automated validation gates
What it does well
- Testing and validating AI agent code changes before merge
- Recording auditable evidence of agent work and decisions
- Running deterministic replay experiments with different coding agents
- Executing dependency-ordered backlogs with AI agents
- Comparing coding agent output using structured review artifacts
Integrations
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Frequently Asked Questions
- Is Conversations in AI Coding Agent free?
- Conversations in AI Coding Agent offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is Conversations in AI Coding Agent open source?
- Yes. Conversations in AI Coding Agent is open source.
- Can I self-host Conversations in AI Coding Agent?
- Yes. Conversations in AI Coding Agent supports self-hosting on your own infrastructure.
- What platforms does Conversations in AI Coding Agent support?
- Conversations in AI Coding Agent is available on: Cross-platform (Python).
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Most coding-agent workflows end with a pull request and a prayer — the agent ran, something changed, and now you are reviewing a diff without knowing what the agent validated, skipped, or decided along the way. Orbit structures that process into what the vendor calls an ‘orbit’: a single task pulled from a dependency-aware backlog, executed through a connected coding agent, gated by tests, lint, and type checks, and closed only when validation passes. Each run produces four artifacts: `agent-result.json` capturing status, changed files, and raw agent output; `evaluation.json` with rubric scoring across task focus, completion, diff signal, and validation; `review.json` with an accept, iterate, or stop recommendation; and `progress.md` as a human-readable mission log.
The differentiating feature is the artifact contract, not the agent integration. Because every run — regardless of which agent ran — produces the same four structured files, teams comparing Claude against Codex are comparing JSON against JSON rather than eyeballing two different diffs. The vendor page describes this as ‘adapter experiments’: swap the agent behind the same contract and compare artifacts instead of anecdotes. The deterministic replay demo, which requires no API key, demonstrates the full loop — task selection, agent path, validation, evidence recording — so the harness behavior is verifiable before any live agent is connected.
Orbit fits teams that need audit trails for agent-generated code changes, researchers running controlled comparisons across coding agents, and repositories where a failing test or lint issue should block task completion rather than just generate a comment. It does not fit teams that need a hosted interface, multi-user access controls, or a pre-built CI/CD integration — the docs describe the project as ‘intentionally small,’ and community contributions are explicitly invited to extend adapter and mission-template coverage. Teams with those requirements will assemble their own scaffolding around the harness or reach for a more opinionated platform.
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