Cadenya
Freemium
Developers building agentic workflows
Preseason.ai
FreeOpen SourceSelf-HostedAgentic
Agent-generated code that passes the vibe check and fails the test suite — then nobody can explain what the agent actually did or why — is the problem Orbit was built around. It is an open-source harness that wraps any AI coding agent in a validation loop: one task, real checks, machine-readable evidence, no hand-waving.
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License: MIT Any use incl. commercial
Local-run terms: MIT license permits unrestricted use, modification, and commercial deployment with attribution.
Pricing
- Model
- Free
Summary
Orbit sits between your backlog and your coding agent, selecting one dependency-ordered task at a time, running the agent, then forcing the result through tests, lint, and type checks before marking the task done. Every run writes structured JSON artifacts — what the agent returned, how the output scored against a rubric, whether a human should accept or iterate — so you are reviewing evidence, not trusting a diff. The agent-neutral contract means you can run Claude, Codex, and Cursor against the same task and compare artifacts instead of impressions. The harness is intentionally minimal; it does not schedule, it does not host, and it does not manage secrets — which means the moment your workflow needs cross-repo coordination or cloud execution, you are writing the glue yourself.
Bottom line: Run Orbit when you need objective, reproducible proof that an agent actually fixed the failing test — not anecdotal confidence — but expect to build your own layer the moment tasks span multiple repositories or require anything beyond local CLI execution.
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Brain Memory
Free · Open source
Long-term continuity in multi-session coding projects
Pros
- Validation gates enforce proof before task completion, so a coding agent cannot mark a fix done while tests are still failing — which eliminates the silent regression problem that plagues unguarded agent loops.
- Agent-neutral adapter contract means you can run Claude, Codex, and Cursor against identical tasks and compare structured evaluation artifacts, so you stop arguing about which agent is better and start looking at data.
- Four machine-readable artifacts per orbit (agent result, evaluation, recommendation, progress log) give audit teams a complete, inspectable record of what the agent returned and how validation scored it — without relying on anyone's memory of what happened.
- Dependency-ordered backlog selection keeps each agent run focused on one unblocked task, which means agents cannot start work that depends on incomplete prior steps — a failure mode that costs hours of untangling in unconstrained agent loops.
- Deterministic replay with no API key required means you can verify the harness behavior itself in isolation, so debugging a broken validation run does not require burning API credits or standing up a live agent.
Cons
- Orbit has no scheduler, no cloud execution layer, and no cross-repo awareness — the moment your workflow requires tasks that span more than one repository or need to run on remote infrastructure, you are assembling that plumbing yourself on top of the harness.
- The adapter contract requires agents to speak JSON over CLI, so agents with browser-only or proprietary API interfaces need a wrapper built before they can run inside an orbit — that wrapper is not provided and is the team's responsibility to maintain.
- Orbit has no built-in backlog management UI or integration with issue trackers; the backlog is whatever structured input you feed it, which means teams used to Jira or Linear-driven workflows will spend setup time before the first orbit runs.
- Teams that need parallel agent execution — running multiple tasks simultaneously to cut wall-clock time on large backlogs — will hit the single-orbit-at-a-time model as a hard ceiling and switch to a purpose-built agent orchestration platform rather than extending Orbit.
About
- Platforms
- Linux, macOS, Windows (CLI/Python-based)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- June 8, 2026
Best For
Who it's for
- Teams building with multiple AI coding agents who need objective comparison
- Development workflows requiring proof-based validation before code acceptance
- Projects with strict test coverage and linting requirements
- Organizations needing audit trails and reproducible agent execution
- Research into agentic coding patterns and harness engineering
What it does well
- Self-healing repositories with failing tests as entry points for agent-driven fixes
- Comparing different coding agents (Claude, Codex, Cursor) against the same benchmarks
- Dependency-ordered task execution with validation gates preventing incomplete work
- Auditing agent activity with machine-readable artifacts and progress logs
- Deterministic replay and debugging of agent sessions without vendor lock-in
Integrations
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Frequently Asked Questions
- Is Preseason.ai free?
- Preseason.ai offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is Preseason.ai open source?
- Yes. Preseason.ai is open source.
- Can I self-host Preseason.ai?
- Yes. Preseason.ai supports self-hosting on your own infrastructure.
- What platforms does Preseason.ai support?
- Preseason.ai is available on: Linux, macOS, Windows (CLI/Python-based).
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Orbit functions as a control harness for AI coding agents, not a coding agent itself. The core workflow: Orbit reads a dependency-ordered backlog, selects the next unblocked task, invokes a configured agent adapter (Claude, Codex, Cursor, or any JSON-speaking CLI), and then runs the actual test suite, linter, and type checker against the output. If validation fails, the task does not advance. Every completed orbit — pass or fail — produces four artifacts: a structured agent result, a rubric-scored evaluation, an accept/iterate/stop recommendation, and a human-readable progress log.
The differentiating design choice is what the vendor page calls ‘proof before close’: the harness does not trust agent output, it verifies it. Failing tests or lint issues become the entry point — you give Orbit a broken state and require it to produce evidence of a fixed state before the task is marked complete. This makes Orbit directly useful for self-healing repository workflows, where the definition of done is a passing test suite rather than a plausible-looking diff. The deterministic replay demo (run with `MOCK=1 ./replay.sh auth-rescue`) demonstrates a full orbit — task selection, agent path, validation, artifact recording — with no API key required, so you can audit the harness behavior before connecting any live agent.
Orbit fits tightly scoped, local, single-repository workflows where reproducibility and audit trails matter more than throughput. It is MIT-licensed, self-hosted by design, and the vendor page describes it as ‘intentionally small.’ That scope is a deliberate constraint, not an oversight — contributions are explicitly directed at making the harness easier to verify, replay, or connect to other tools, not at expanding its surface area. Teams that need cloud execution, multi-repo coordination, or a scheduler will find none of that here and will need to wire Orbit into a broader system themselves.
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