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AICTL
Pricing
- Model
- Free
Summary
AI coding agents that look great in isolation fall apart when you cannot tell whether they actually fixed the test, changed the right files, or just hallucinated a plausible diff. Orbit is a local harness that wraps each agent run in a bounded cycle with real validation gates and a durable evidence trail.
Each 'orbit' is one task: the harness selects it from a dependency-ordered backlog, runs the agent, then requires passing tests, lint, and type checks before closing the loop — no proof, no progress. Every run produces structured JSON artifacts (agent output, rubric scoring, a human-readable progress log) that you can inspect or replay without re-running the agent. The deterministic replay demo runs without an API key, so you can see the full cycle before wiring in a real model. Orbit is intentionally small — no hosted infrastructure, no GUI — which keeps it auditable and keeps you in control, but also means everything outside the core loop is your problem to build.
Bottom line: Pick Orbit when you need reproducible, auditable agent runs on a local codebase; plan on building your own scheduling, notification, and CI integration layers when the harness needs to scale beyond a single developer's machine.
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Pros
Sign in to edit- Validation gates (tests, lint, type checks) block task completion until the agent proves its work, so you stop merging diffs that pass a visual review but break the build.
- Dependency-ordered backlog selection keeps each run scoped to one task at a time, which means agents cannot skip prerequisites and produce output that assumes work that was never done.
- All four run artifacts are inspectable JSON and Markdown, so a post-mortem on a failed agent run takes minutes instead of reconstructing what happened from logs.
- Agent-neutral adapter contract lets you run the same task against different coding agents and compare structured evaluation scores — replacing 'it felt better' with actual rubric data.
- Deterministic replay runs without an API key, so you can validate the full harness loop in a new environment before spending any API budget.
Cons
Sign in to edit- There is no REST API, hosted runtime, or scheduler: every orbit runs locally from the command line. Teams that need to trigger runs from a CI pipeline or across multiple machines have to wire that infrastructure themselves before Orbit is production-useful.
- The harness is intentionally minimal — no web UI, no notification system, no multi-repo coordination. When a team needs to manage more than a handful of concurrent agent tasks or wants a dashboard for non-engineering stakeholders, Orbit's output artifacts are not enough and teams move to a fuller platform rather than extending the harness.
- Adapter support depends on community contributions; if your agent does not already have an adapter and does not speak JSON on the CLI, you write the adapter yourself before the first orbit runs — there is no plug-and-play path for proprietary or GUI-only tools.
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About
- Platforms
- Linux, macOS, Windows (Python)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-01T07:01:39.264Z
Best For
Who it's for
- Teams building agentic coding workflows needing reproducibility
- Evaluating multiple coding agents against the same task contracts
- Implementing deterministic validation gates for AI-generated code
- Maintaining durable audit trails and progress logs for agent work
- Local harness development with no vendor lock-in
What it does well
- Self-healing repositories with automatic test validation gates
- Dependency-ordered task backlog execution for coding agents
- Multi-agent adapter experiments and comparative evaluation
- Deterministic replay and audit trails for agent work verification
- Bounded task execution with human review checkpoints
Integrations
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Frequently Asked Questions
- Is AICTL free?
- Yes — AICTL is fully free to use. There is no paid tier.
- Is AICTL open source?
- Yes. AICTL is open source.
- Can I self-host AICTL?
- Yes. AICTL supports self-hosting on your own infrastructure.
- What platforms does AICTL support?
- AICTL is available on: Linux, macOS, Windows (Python).
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Curated lists that include this category
Most coding-agent setups have no memory of what happened last run and no mechanism to reject work the agent cannot prove it completed. Orbit addresses this by turning each task into a bounded execution cycle: it selects one task from a dependency-ordered backlog, invokes the agent, runs validation gates (tests, lint, type checks), and only advances the backlog when the gates pass. The run produces four artifacts — agent-result.json, evaluation.json, review.json, and progress.md — that record what changed, how the work scored against a rubric, and what the recommended next action is.
The standout design decision is agent-neutrality. Orbit speaks to Claude, Codex, Cursor, or any JSON-capable CLI through the same adapter contract, which means you can run the same task against two different agents and compare the artifact files rather than relying on anecdotal impressions. The vendor describes this as ‘adapter experiments’ — swapping agents behind the same task contract. For teams evaluating which model or tool handles their codebase best, this is the differentiating capability.
Orbit fits teams building agentic coding workflows who need replay, auditability, and human review checkpoints before agent output lands in a branch. It does not provide a hosted runtime, a web UI, a REST API, or a scheduler — the vendor page is explicit that the tool is intentionally small. Teams that outgrow local execution or need to run orbits across multiple machines in CI will find the harness is a foundation, not a finished system. MIT-licensed and self-hosted only, with no commercial tier and no vendor infrastructure dependency.