Cadenya
Freemium
Developers building agentic workflows
LocalFlow
FreeOpen SourceSelf-HostedAgentic
AI coding agents that pass the demo and fail the audit are the exact problem — you get a diff, maybe a passing run, no trail of what actually happened or why. Orbit is an open-source harness that closes that gap by wrapping each agent task in a validation gate and writing inspectable evidence before marking anything complete.
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License: MIT Any use incl. commercial
Local-run terms: MIT license permits unrestricted use, modification, and distribution for any purpose including commercial, with no royalties or restrictions.
Pricing
- Model
- Free
Summary
The core loop is deliberately small: Orbit selects one dependency-ordered task, hands it to whichever coding agent you wire in, runs tests, lint, and type checks, and only closes the task if the agent can prove the work passed. Every run produces four artifact files — structured result JSON, rubric-scored evaluation, a review recommendation, and a human-readable progress log. That paper trail is what lets you compare two agents on the same task by diffing artifacts instead of re-running demos. The harness runs locally with no API key required for the replay demo, so there is nothing to provision before you can see it work. The ceiling appears fast on non-coding tasks — Orbit is built for code-output validation and nothing else.
Bottom line: Orbit is the right harness when you need proof-of-work artifacts and a reproducible audit trail for coding agent output — it is not the right choice when your workflow involves document generation, multi-modal outputs, or agent tasks that do not reduce to test-passing diffs.
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Brain Memory
Free · Open source
Long-term continuity in multi-session coding projects
Pros
- Validation gates require passing tests, lint, and type checks before a task closes, so agent output that compiles but breaks the suite cannot advance silently through your backlog.
- Four structured artifact files written per run — result, evaluation, review, and progress log — so post-run audits and team reviews have a consistent schema to diff rather than agent-specific output formats.
- Agent-neutral JSON contract means swapping Claude for Codex behind the same harness is an adapter change, not a rewrite, so agent comparison runs on identical tasks produce directly comparable evidence.
- Dependency-aware backlog selection keeps each orbit focused on one task at a time, so the harness does not hand the agent an ambiguous multi-task bundle that obscures which step caused a failure.
- Fully local execution with no API key required for the replay demo, so you can inspect the full artifact pipeline and harness behavior without provisioning any cloud credentials.
Cons
- Validation is gated on tests, lint, and type checks — tasks that do not produce a testable code diff have no validation signal the harness can use, and teams building agents for document generation or non-code outputs hit this ceiling immediately and route to a different framework.
- The harness is intentionally small with no built-in agent execution runtime; teams that need scheduling, parallel agent runs, or cloud-hosted execution have to build that infrastructure themselves or move to a hosted agent platform that includes it.
- There is no API surface described in the vendor page, which means integrating Orbit into an existing CI pipeline or orchestrating it from another system requires direct shell invocation or script wrapping — teams with complex pipeline requirements end up owning that glue code permanently.
About
- Platforms
- Linux, macOS, Windows (Python-based)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- June 3, 2026
Best For
Who it's for
- Agent developers testing harness patterns
- Teams evaluating multiple coding agents
- Projects requiring proof-of-work artifacts and audit trails
- Local agent experimentation without cloud APIs
- Deterministic agent workflow validation
What it does well
- Validating AI agent code generation with deterministic test gates
- Comparing different coding agents on the same bounded tasks
- Building self-healing repositories that require proof before task closure
- Replicating and auditing agentic workflows through durable progress logs
- Experimenting with agent harness patterns in local development
Integrations
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Frequently Asked Questions
- Is LocalFlow free?
- LocalFlow offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is LocalFlow open source?
- Yes. LocalFlow is open source.
- Can I self-host LocalFlow?
- Yes. LocalFlow supports self-hosting on your own infrastructure.
- What platforms does LocalFlow support?
- LocalFlow is available on: Linux, macOS, Windows (Python-based).
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Most agent harnesses tell you a task completed. Orbit requires the agent to prove it. The workflow is a bounded loop: backlog selection picks one task respecting declared dependencies, the agent runs against it, and then tests, lint, and type checks decide whether the orbit closes or retries. If the agent cannot produce passing validation, the task stays open. The result is not just a status flag — it is four artifact files written to disk: agent-result.json capturing what the agent returned and which files changed, evaluation.json with rubric scores across task focus and diff signal, review.json with an accept-or-iterate recommendation, and progress.md as a human-readable mission log.
The differentiating feature is agent-neutrality enforced through a JSON contract. The docs describe support for Claude, Codex, and Cursor as interchangeable adapters behind the same harness interface. That means comparing two coding agents on identical bounded tasks produces comparable artifacts — you are reading the same schema from both runs, not reconciling two different output formats. Community contributions follow the same adapter pattern, so plugging in a new agent does not require rewriting the validation layer.
Orbit fits local agent experimentation, self-healing repository setups where failing tests are the starting state, and any project where an audit trail of what changed and why is a hard requirement. The intentional smallness the vendor describes is also a real constraint: the harness validates code-output tasks through test gates. Tasks that do not produce a testable diff — content generation, data transformation without assertions, UI work without a test suite — have no natural validation signal here, and the gate layer offers precious little to grab onto.
The replay demo runs without any API key using a MOCK flag, which means the harness and artifact pipeline are fully inspectable before you wire in a live agent. The project is MIT licensed with source on GitHub, and the vendor explicitly frames contributions around making the harness easier to verify or replay rather than expanding scope.
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