Cadenya
Freemium
Developers building agentic workflows
ProData AI
FreeOpen SourceSelf-HostedAgentic
Coding agents that pass the demo fail the audit — they run, they change files, and nobody can tell you whether the test suite passed or why the task was marked done. Orbit exists for that gap.
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Get This Tool
License: MIT Any use incl. commercial
Local-run terms: Users can clone, install, and run Orbit locally for any purpose, including commercial use, under the MIT license.
Pricing
- Model
- Free
Summary
Orbit is an open-source harness that wraps AI coding agent runs in a fixed loop: pick a task from a dependency-ordered backlog, run the agent, validate the output against tests, lint, and type checks, then record structured evidence before the task closes. Nothing advances without proof. Each run produces four artifact files — agent output, rubric scores, a recommendation, and a human-readable log — so you can inspect exactly what happened without replaying the whole session. The harness is agent-neutral; Claude, Codex, Cursor, or any JSON-speaking CLI plugs in behind the same contract. The ceiling appears quickly on teams who need anything beyond the validation-gate model — custom orchestration, parallel agent execution, or UI-driven workflow design are not in scope.
Bottom line: The right call for teams who want a traceable, self-healing coding loop with a single agent and a failing test suite; the wrong call the moment you need parallel agent execution or a workflow canvas that non-engineers can configure.
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Brain Memory
Free · Open source
Long-term continuity in multi-session coding projects
Pros
- Validation gates block task completion until tests, lint, and type checks pass, so an agent cannot silently mark work done on a broken diff — the kind of silent failure that compounds across a backlog.
- Four structured artifact files per run (agent output, rubric scores, recommendation, progress log), which means you have a concrete audit trail when something goes wrong instead of reconstructing what the agent did from git history.
- Agent-neutral JSON contract, so switching the underlying coding agent — from Claude to Codex or a local model — does not require rewiring the workflow, which means you can benchmark agents against the same task set and compare artifacts instead of impressions.
- Dependency-ordered backlog selection keeps each run focused on one task at a time, which prevents agents from scope-creeping across unrelated files and makes the diff signal meaningful.
- MIT-licensed and self-hostable with no external API required for the replay demo, so you can evaluate the full loop and inspect what it records without exposing credentials or production code to a third-party service.
Cons
- Parallel agent execution is not in scope — the harness runs one orbit at a time in sequence. Teams with large backlogs who need multiple agents working concurrently will find Orbit serializes what their workflow requires to parallelize, and they will either script around it or move to a purpose-built multi-agent orchestration layer.
- There is no UI, no workflow canvas, and no non-engineer interface. Configuration is CLI and JSON. A product manager or QA lead who needs to inspect or adjust the backlog without engineering support cannot do so — teams in that situation add a wrapper or abandon the tool for something with a visual layer.
- The artifact schema and rubric scoring are fixed by the harness design. Teams with domain-specific validation requirements beyond tests, lint, and type checks — for example, semantic correctness checks or business-rule assertions — must write custom adapter logic. The docs describe this as a contribution path, but it is engineering work that falls outside the core harness.
About
- Platforms
- Python (CLI), agent-agnostic
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- June 6, 2026
Best For
Who it's for
- Teams automating code generation with multiple agents
- Development workflows requiring validation before task completion
- Comparing agent performance across standardized benchmarks
- Projects needing audit trails and evidence of agent execution
- Iterative agentic coding with human review checkpoints
What it does well
- Running AI agents on failing tests with proof of completion
- Managing dependency-ordered task backlogs with agent automation
- Comparing coding agents using standardized validation gates
- Recording durable evidence artifacts for auditing agent work
- Building self-healing codebases with autonomous linting and type checking
Integrations
Frequently Asked Questions
- Is ProData AI free?
- ProData AI offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is ProData AI open source?
- Yes. ProData AI is open source.
- Can I self-host ProData AI?
- Yes. ProData AI supports self-hosting on your own infrastructure.
- What platforms does ProData AI support?
- ProData AI is available on: Python (CLI), agent-agnostic.
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Most AI coding runs disappear into a terminal. You know the agent touched files; you do not know whether the change was correct, what tests it passed, or how the next task should proceed. Orbit structures that loop: it selects a task from a dependency-ordered backlog, routes it to a coding agent, runs validation gates — tests, lint, type checks — and refuses to close the task unless the agent can prove completion. Every orbit produces four structured artifacts: agent-result.json (what the agent returned and which files changed), evaluation.json (rubric scores across task focus, diff signal, and validation), review.json (accept, iterate, or stop recommendation), and progress.md (a human-readable mission log).
The differentiating feature is the validation gate as a hard stop, not a suggestion. The vendor states the design principle directly: if the agent cannot prove it, the orbit does not close. That makes Orbit useful for self-healing repository workflows — feed it a backlog of failing tests or lint violations, and the harness advances only the orbits that survive the checks. Because the contract is agent-neutral JSON, teams can swap the underlying coding agent and compare artifacts across runs rather than comparing impressions.
Orbit fits a specific wedge: small-to-medium development teams who want auditable, iterative coding automation and are willing to work at the CLI level. It is intentionally scoped. The docs describe it as ‘intentionally small,’ and community contribution guidance focuses on making the harness easier to verify and replay — not on expanding surface area. Teams that need a workflow canvas, parallel agent coordination, or non-engineer-facing configuration will hit the ceiling of what the harness expresses.
Orbit is MIT-licensed and self-hosted only — there is no hosted API or managed cloud offering. The deterministic replay demo (MOCK=1 ./replay.sh auth-rescue) runs without an API key and shows the full select-validate-record loop, which makes the evaluation path clear before any agent credentials are involved.
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