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Mnemo
Pricing
- Model
- Free
Summary
AI coding agents look disciplined in a demo and then quietly accept their own broken output in production — no test run, no lint check, just a changed file and a closed task. Orbit is the harness that refuses to let that happen.
Orbit wraps each agent run in a bounded loop: it selects a dependency-ordered task from your backlog, hands it to whichever coding agent you point at it, then runs tests, lint, and type checks before the task is allowed to close. Every run leaves 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 agent-neutral contract means you can swap Claude for Codex behind the same harness and compare artifacts instead of gut feelings. Where Orbit hits its ceiling: it is a harness, not a planner, so teams that need autonomous task decomposition or cross-repo coordination will be adding that layer themselves.
Bottom line: Run Orbit when you need a deterministic, auditable record of what an AI coding agent actually proved before a task closed — but plan for additional tooling the moment your workflow requires agents that decompose their own goals rather than execute a pre-ordered backlog.
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Pros
Sign in to edit- Validation gates run tests, lint, and type checks before a task closes, so broken output cannot silently pass — without this, an agent marks work complete on a diff that fails your own test suite.
- Four structured artifacts per run (agent result, rubric evaluation, review recommendation, progress log), which means an audit of what the agent proved is always available without reconstructing the run from memory or logs.
- Deterministic replay with no API key required, so you can compare two models against the same task by comparing their JSON artifacts — replacing 'it worked in my demo' with a side-by-side diff.
- Agent-neutral JSON contract, so switching from one coding agent to another is an adapter swap, not a workflow rewrite — teams that need to evaluate models against real tasks do not have to rebuild the harness each time.
- Dependency-aware backlog selection keeps each run focused on one task, which means the agent cannot wander into adjacent work and produce a diff that touches three things you did not ask for.
Cons
Sign in to edit- Orbit expects a pre-structured, dependency-ordered backlog — it does not decompose goals into tasks. Teams whose actual problem is 'figure out what to build next' hit this wall immediately and have to build or buy a planning layer before Orbit adds any value.
- There is no hosted option and no API surface, which means every team that wants Orbit in a CI pipeline or a shared environment is running their own infrastructure. For a solo project this is fine; for an organization that wants a shared validation service across multiple repos, the ops burden lands entirely on the team.
- The harness is intentionally small and community-contributed — the docs explicitly describe it as such. Teams that need adapters for agents not already supported write the adapter themselves, and teams that hit edge cases in the validation loop are filing issues against a project with no commercial support tier, which is the condition under which teams with production SLAs move to a vendor-backed tool instead.
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About
- Platforms
- Cross-platform (Python)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-06T04:17:54.906Z
Best For
Who it's for
- Teams validating AI-generated code automatically
- Deterministic testing of coding agent behavior
- Organizations requiring audit trails for AI work
- Comparing agent implementations across models
- Self-correcting development workflows
What it does well
- Self-healing repositories requiring proof before task completion
- Backlog-driven task execution with dependency ordering
- Comparing coding agent outputs across different models
- Auditing AI agent work with deterministic replay capability
- Validating agent output before accepting changes
Integrations
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Frequently Asked Questions
- Is Mnemo free?
- Yes — Mnemo is fully free to use. There is no paid tier.
- Is Mnemo open source?
- Yes. Mnemo is open source.
- Can I self-host Mnemo?
- Yes. Mnemo supports self-hosting on your own infrastructure.
- What platforms does Mnemo support?
- Mnemo is available on: Cross-platform (Python).
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Curated lists that include this category
Orbit sits between your backlog and your coding agent, enforcing a contract the agent cannot skip. A task enters, the agent runs, and then validation gates — tests, lint, type checks — decide whether the orbit closes or the loop repeats. The output is not just a code diff: every run produces four durable artifacts capturing the agent’s raw output, a rubric-scored evaluation, a human-reviewable recommendation, and a progress log. The vendor describes this as ‘bounded, validated, auditable loops,’ and the structure holds even when the agent fails — the failure is recorded and the task stays open.
The differentiating feature is deterministic replay. The docs describe a replay demo that requires no API key and can be run locally in minutes, executing a full orbit — task selection, agent path, validation, evidence recording — against a fixed scenario. For teams that need to audit AI work or compare how two different models handle the same task, this means the comparison is artifact-to-artifact, not demo-to-demo.
Orbit fits tightest in two scenarios: self-healing repositories where failing tests or lint issues are handed directly to the agent with proof-of-fix required before completion, and backlog-driven workflows where dependency ordering matters and you want one verified step at a time. It does not fit when the problem is task decomposition — Orbit expects the backlog to already exist, structured and ordered. Teams that need an agent to figure out what to do, not just do it, will find Orbit provides no planning layer. At that point the choice is to build the planner on top or move to a tool that bundles both.
Orbit is MIT licensed, self-hosted, and agent-neutral by design — the docs cite Claude, Codex, and Cursor as compatible, with the integration surface being any JSON-speaking CLI. There is no API and no hosted offering; the harness runs where you run it.