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SIMD Agent
Pricing
- Model
- Free
Summary
AI coding agents run a task, return output, and leave you guessing whether the work was actually done — no proof, no audit trail, no way to tell if the tests passed or the agent hallucinated a fix.
Orbit is an MIT-licensed open-source harness that wraps any JSON-speaking CLI agent — Claude, Codex, Cursor, or otherwise — in a bounded loop: select one task from a dependency-aware backlog, run the agent, gate on real validation (tests, lint, type checks), and write inspectable artifacts before closing the orbit. Every run produces four JSON/markdown files recording what the agent returned, how the output scored against a rubric, whether to accept or iterate, and a human-readable mission log. The harness is intentionally small, so there is precious little abstraction to hide behind — what you see is what runs. Teams with strict audit requirements get durable, reviewable evidence without instrumenting the agent itself. The trade-off is that Orbit is a harness framework, not a turnkey product: you bring the agent, the backlog structure, and the validation suite.
Bottom line: Orbit earns its place in any repo where 'the agent said it passed' is not good enough — but teams that need a hosted dashboard, multi-repo orchestration, or a UI for non-engineers will hit the edges of what a CLI harness can carry.
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Pros
Sign in to edit- Agent-neutral adapter contract, so you can swap Claude for Codex or any other JSON-speaking CLI behind the same harness without rewriting your validation logic or losing artifact continuity.
- Validation gates block task completion until tests, lint, and type checks pass, which means 'the agent said it worked' is never the acceptance criterion — proof is.
- Dependency-aware backlog selection keeps each orbit scoped to one task at a time, so the agent cannot drift into adjacent work and leave the codebase in a half-finished state.
- Structured artifact output per run — four files covering result, evaluation, review recommendation, and progress log — so audit trails and agent comparison experiments run on inspectable data rather than stdout logs.
- MIT-licensed and self-hostable with no commercial dependency, so the harness can run inside air-gapped or regulated environments where a SaaS agent platform is a non-starter.
Cons
Sign in to edit- Orbit produces no UI — review artifacts are JSON and markdown files on disk. Teams where product managers or compliance officers need to review agent work without opening a terminal hit this wall immediately and end up building a separate reporting layer.
- The validation gates are only as strong as the suite you bring: a codebase with no tests, no lint config, and no type checks gives Orbit nothing to gate on, which means the bounded-loop guarantee collapses to 'the agent returned output' — the same problem Orbit exists to solve.
- Backlog and task structure require manual definition in a format the harness expects; there is no backlog ingestion from issue trackers, project management tools, or CI systems. Teams running high-velocity sprints from Jira or Linear spend engineering time on a translation layer, and when that overhead compounds, they switch to an agent platform with native integrations.
- There is no API surface — the tool is CLI-only — so embedding Orbit into a larger automated pipeline (CI/CD, event-driven triggers, multi-repo workflows) requires shell scripting around the harness rather than programmatic control.
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About
- Platforms
- Python 3, Linux, macOS
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-01T06:37:30.882Z
Best For
Who it's for
- Teams deploying AI coding agents in production codebases
- Projects requiring validation evidence and audit trails
- Experiments comparing agent implementations or models
- Repositories with strict validation requirements (tests, linting, type checks)
- Developers building agent harnesses and evaluation frameworks
What it does well
- Running self-healing repositories with automated validation before task completion
- Executing ordered backlogs of coding tasks with dependency tracking
- Comparing different AI coding agents on the same contract and metrics
- Recording auditable evidence of agent work for compliance and review
- Building deterministic, repeatable AI coding workflows
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Frequently Asked Questions
- Is SIMD Agent free?
- Yes — SIMD Agent is fully free to use. There is no paid tier.
- Is SIMD Agent open source?
- Yes. SIMD Agent is open source — the source repository is at https://github.com/simd-ai/agent.
- Can I self-host SIMD Agent?
- Yes. SIMD Agent supports self-hosting on your own infrastructure.
- What platforms does SIMD Agent support?
- SIMD Agent is available on: Python 3, Linux, macOS.
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Curated lists that include this category
Most AI coding agent demos end at the diff. Orbit picks up where they stop: after the agent returns output, the harness runs your actual test suite, lint, and type checks — and if validation fails, the orbit does not close. The core workflow is a loop: Orbit reads a dependency-ordered backlog, selects the next eligible task, hands it to whichever CLI agent is configured, collects structured output, runs the configured validation gates, writes four artifact files, and either advances or flags for human review. The deterministic replay demo — requiring no API key — lets you inspect this entire cycle locally before connecting a live agent.
The differentiating feature is the artifact layer. Every orbit produces `agent-result.json` (structured status, changed files, raw agent output), `evaluation.json` (rubric scoring across task focus, completion, diff signal, and validation), `review.json` (accept, iterate, or stop recommendation), and `progress.md` (a human-readable mission log). This means compliance reviews, retrospectives, and agent comparisons work from evidence rather than memory — you can diff two agents’ evaluation scores on the same task contract without trusting anyone’s anecdote.
Orbit fits tightest in codebases that already have a working test suite and lint configuration, because the validation gates are only as strong as the checks you wire in. It fits least in projects that need a hosted interface, webhook integrations, or multi-repo coordination — the vendor page describes the project as intentionally small, and the contribution guide explicitly targets contributors who want to make the harness easier to verify or replay, not to grow its surface area. Teams that outgrow the CLI harness model — because stakeholders need a browser dashboard or because the backlog spans more than one repository — are the ones who move to a purpose-built agent platform.