Cadenya
Freemium
Developers building agentic workflows
Tabbit
FreeOpen SourceAgentic
Agentic coding loops fail silently — the agent says it's done, the diff looks plausible, and you only find the broken test three PRs later. Orbit is a MIT-licensed harness that refuses to close a task unless the agent can prove the work passed.
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License: MIT Any use incl. commercial
Local-run terms: MIT license allows free use, modification, and distribution in commercial and private projects with attribution.
Screenshots 2
Summary
Orbit wraps agent execution in bounded, dependency-ordered tasks: one unit of work at a time, with tests, lint, and type checks acting as the gate before progress is recorded. Every run produces four structured artifacts — result JSON, rubric evaluation, a review recommendation, and a human-readable progress log — so code review has evidence instead of vibes. The agent-neutral contract means you can swap Claude, Codex, or Cursor behind the same harness and compare artifacts on identical task sets. The ceiling appears fast: Orbit is deliberately small, so teams that need scheduling across distributed workers or CI/CD pipeline integration will be adding that infrastructure themselves. It is a harness, not a platform.
Bottom line: Pick Orbit when you need auditable proof that an agent's output passed validation before it merged; plan for additional infrastructure work when your workflow grows beyond single-machine, sequential task execution.
Pricing Plans
FreeOpen Source
Free
Full open-source harness with all features
- Bounded task execution
- Validation gates (tests, lint, type checks)
- JSON evidence recording
- Deterministic replay demo
- Dependency-aware backlog selection
- Agent-neutral design
View full pricing on tabbit.ai →
Pricing may have changed since last verified. Check the official site for current plans.
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Brain Memory
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Long-term continuity in multi-session coding projects
Pros
- Validation gates block task completion until tests, lint, and type checks pass, which means broken code cannot advance the backlog the way it does in agent workflows that trust self-reported completion.
- Four structured artifact files are written per orbit, so code review and compliance audits have machine-readable evidence of what the agent did — instead of reconstructing intent from commit messages.
- Agent-neutral adapter contract means you can run Claude, Codex, and Cursor against the same task set and compare evaluation JSON directly, replacing informal 'which agent felt better' conversations with recorded rubric scores.
- MOCK mode runs the full select-validate-record loop without an API key, so teams can test harness logic, build new adapters, and reproduce past runs in air-gapped or cost-sensitive environments.
- Dependency-ordered backlog selection keeps each orbit to one bounded task, which means the agent is not trying to hold an unbounded context window across a sprawling multi-step job — a common source of drift in longer agentic runs.
Cons
- Orbit executes tasks sequentially on a single machine. Teams that need parallel agent runs across a distributed backlog hit this wall as soon as they move beyond single-developer experimentation — at which point they are writing their own scheduling layer on top of the harness.
- There is no hosted API, webhook integration, or CI/CD trigger mechanism described on the vendor page. Connecting Orbit to a GitHub Actions workflow or a pull-request queue requires custom glue code; teams with existing automation pipelines will be building that bridge from scratch.
- The harness is MIT-licensed and intentionally minimal, with no commercial support tier. Teams that need guaranteed response time on bugs or security patches in a production compliance context will switch to a vendor-supported orchestration framework — Orbit's contribution model is community-driven, not SLA-backed.
About
- Platforms
- Linux, macOS, Windows (Python 3.8+)
- API Available
- No
- Self-Hosted
- No
- Last Updated
- June 1, 2026
Best For
Who it's for
- Teams running agentic coding workflows with validation requirements
- Evaluating and comparing different AI coding agents on real tasks
- Building reproducible and auditable agent-assisted development pipelines
- Self-healing codebases that require proof of test passage before completion
- Research and experimentation on agent harness design
What it does well
- Validate AI-generated code with tests and lint checks before task completion
- Execute dependency-ordered backlogs of coding tasks across multiple agents
- Compare different coding agents (Claude, Codex, Cursor) on the same task set
- Create auditable evidence trails of agent work for code review and compliance
- Run deterministic simulations of agent workflows without external API calls
Integrations
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Frequently Asked Questions
- Is Tabbit free?
- Tabbit offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is Tabbit open source?
- Yes. Tabbit is open source.
- What platforms does Tabbit support?
- Tabbit is available on: Linux, macOS, Windows (Python 3.8+).
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Orbit positions itself as mission control for agentic coding workflows: it selects a task from a dependency-ordered backlog, hands it to whatever coding agent you point at it, runs tests and lint as validation gates, and records the full evidence trail as inspectable JSON before marking the orbit closed. The vendor page describes this as a ‘bounded, validated, auditable loop’ — the agent does not advance until the checks pass. A deterministic replay demo ships with the repo and runs without an API key, so you can watch the full select-run-validate-record cycle locally before connecting any external model.
The differentiating mechanic is the artifact set. Each completed orbit produces four files: `agent-result.json` (structured output, changed files, raw agent response), `evaluation.json` (rubric scoring across task focus, completion, diff signal, and validation), `review.json` (accept/iterate/stop recommendation), and `progress.md` (human-readable mission log). This means code review and compliance audits have a durable record of what the agent claimed, what the checks proved, and what changed — not just a commit.
Orbit is intentionally scoped. It fits teams building self-healing repos where failing tests or lint issues are fed back as tasks requiring proof before closure, and it fits adapter experimentation — swapping agents behind the same contract to compare artifacts instead of running informal A/B tests. Where it breaks: the harness is a single-machine, sequential loop. Teams that need parallel agent execution across a distributed backlog, webhook triggers from CI systems, or a hosted scheduling layer will find none of that here. The vendor page says Orbit is ‘intentionally small’ and asks contributors to make it ‘easier to verify, easier to replay, or easier to connect’ — which is an honest description of what is missing.
The repo ships with a MOCK mode (`MOCK=1 ./replay.sh auth-rescue`) that drives the full workflow without external API calls, making it viable for testing harness logic, writing new adapters, or running in air-gapped environments. Any agent that speaks JSON over CLI can be wired in; the adapter contract is the extension point.
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