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Summary
AI coding agents hallucinate completion — they return output that looks like progress but leaves tests red and lint broken, and you have no record of what actually ran. Orbit closes that gap by wrapping each agent task in a validation loop that refuses to mark work done unless the checks pass.
Orbit runs as a local harness that pulls one dependency-ordered task at a time, hands it to whichever coding agent you configure, then runs your tests, lint, and type checks before recording the result. Every run writes structured JSON artifacts — what the agent returned, how the output scored against a rubric, and a human-readable recommendation to accept, iterate, or stop. The audit trail is durable and replayable without an API key, which makes it usable in air-gapped environments. The tooling is intentionally minimal, so teams building on top of it will write their own adapter glue for agents that do not speak the expected JSON contract. Orbit does not manage the agent itself — it manages what the agent must prove.
Bottom line: Orbit is the right harness when you need a verifiable record that an agent's code changes actually passed validation — and the wrong choice when you need a hosted service, a UI, or anything beyond the CLI loop it deliberately stops at.
Pricing Plans
FreeOpen Source
Full Orbit harness with all features
- Bounded task execution
- Validation gates
- Deterministic replay
- JSON artifact recording
- Progress logs
- Agent-neutral design
View full pricing on memexlab.ai →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Validation gates block task completion until tests, lint, and type checks pass, which means you stop shipping agent output that looks correct but breaks the build.
- Durable, structured artifacts written after every run — including rubric scoring and a human-readable progress log — so you have an audit trail when a stakeholder asks what the agent actually did last Tuesday.
- Deterministic replay with no API key required, so you can rerun any recorded orbit in a local or air-gapped environment without incurring model costs or network dependencies.
- Agent-neutral adapter contract, so swapping Claude for Codex behind the same task backlog produces comparable JSON artifacts instead of anecdotal impressions about which agent performed better.
- Dependency-aware backlog sequencing, which means the harness advances tasks in the order your project actually requires rather than letting an agent jump to a task whose prerequisites are still failing.
Cons
Sign in to edit- Agents that do not return structured JSON output require a custom adapter before Orbit can score or validate them — that wrapper is yours to write and maintain, and the docs describe it as a contribution target rather than a solved problem.
- There is no hosted service, no web UI, and no managed execution layer; teams that need cloud-hosted runs, a visual dashboard, or multi-user access to the artifact store will build all of that infrastructure themselves or switch to a commercial agent orchestration platform that ships those layers.
- The harness is intentionally small, which means complex branching logic — tasks that conditionally fan out based on what a prior agent returned — is outside what Orbit models; teams with multi-path workflows end up scripting the branching outside Orbit and using the harness only for the leaf-level validation step.
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About
- Platforms
- Linux, macOS, Python 3.7+
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-03T08:19:36.479Z
Best For
Who it's for
- Teams building reliable AI coding agent workflows
- Comparative agentic development and agent selection
- Projects requiring proof-based validation and audit trails
- Local-first or self-hosted agent orchestration
What it does well
- Self-healing repos: gate task completion on test and lint validation
- Backlog execution: run dependency-ordered tasks with verified progress
- Agent comparison: swap coding agents and compare artifacts instead of anecdotes
- Agentic workflow tracing: audit and replay agent decisions with durable logs
Integrations
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Frequently Asked Questions
- Is Memex free?
- Yes — Memex is fully free to use. There is no paid tier.
- Is Memex open source?
- Yes. Memex is open source.
- Can I self-host Memex?
- Yes. Memex supports self-hosting on your own infrastructure.
- What platforms does Memex support?
- Memex is available on: Linux, macOS, Python 3.7+.
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Curated lists that include this category
Most agent runs produce a diff and a success message. Whether that diff broke anything is a separate question nobody asked the agent to answer. Orbit is an open-source, MIT-licensed harness built by Human-Again that wraps AI coding-agent execution in bounded, validated loops: one task per orbit, real validation gates before the task closes, and a set of JSON and Markdown artifacts written to disk as evidence after every run.
The defining feature is the validation gate. Orbit does not accept an agent’s self-reported success as completion. Tests, lint, and type checks run on the actual output, and the orbit does not close until they pass — or until the harness records that they failed and flags the task for iteration or human review. The four output files (agent-result.json, evaluation.json, review.json, progress.md) give you a scored, structured record of what happened: what the agent changed, whether the rubric criteria were met, and what the recommended next action is. That record is replayable deterministically, with a demo path that needs no API key at all.
Orbit fits teams who are already running coding agents in CI or local developer loops and need proof-based records rather than trust-based ones — for audit, for debugging, or for comparing two agents against the same task backlog. It is agent-neutral by design; the docs describe adapters for Claude, Codex, Cursor, and any CLI agent that returns JSON. Where it breaks is at the edges of that contract: agents that do not produce structured output need a wrapper written before Orbit can score them, and there is no hosted service, no web UI, and no managed execution environment — just the harness and the shell scripts you run locally or in your own CI.