Mission control for AI coding agents.
Orbit is the harness your coding agent actually needs — structured loops, real validation gates, rubric-based evaluation, checkpoint resumability, and a full audit trail. Define the mission. Orbit lands it.
↑ Orbit detects a failing test, selects the right task, runs the agent, validates the fix, and marks the orbit complete — no hand-holding required.
They drift. They hallucinate changes. They skip validation. They leave no record of what they did or why. You give them a task and cross your fingers.
You: "Fix the auth bug."
Agent: ✅ Done!
You: "Did you run the tests?"
Agent: ✅ All passing!
You: *runs tests* ❌❌❌
Sound familiar?
Every agent run becomes a structured orbit — a bounded, validated, auditable loop. If the agent can't prove its work, the orbit doesn't close.
↑ The Orbit loop: task selection → agent execution → validation gate → rubric evaluation → review → next orbit.
What you get:
- 🎯 Mission files — human-owned goal, scope, constraints, definition of done
- 📋 Dependency-aware task queue — priority-sorted, one task per orbit, respects your DAG
- 🚦 Validation gate — real
pytest/lint/typecheck runs; nothing advances without evidence - 📐 Rubric evaluator — structured scoring on task focus, completion, change signal, and validation
- 🔁 Retry policy — auto-retry on failure or validation miss, configurable limits
- 💾 Checkpoint resumability — durable state; pick up mid-mission without losing progress
- 💰 Budget management — cap runs, failure count, and estimated cost per session
- 📡 Full telemetry — every orbit writes
agent-result.json,evaluation.json,review.json, and a timestamped progress log
Orbit is adapter-based. Swap in whatever coding agent you have:
| Adapter | Agent |
|---|---|
adapters.claude_cli:ClaudeCliAdapter |
Claude Code (claude --print) |
adapters.codex_cli:CodexCliAdapter |
OpenAI Codex CLI |
adapters.cursor_cli:CursorCliAdapter |
Cursor |
adapters.cli_json:CliJsonAdapter |
Any agent that reads stdin and writes JSON |
adapters.mock:MockAgentAdapter |
Local testing / dry runs |
| Use case | How Orbit helps |
|---|---|
| Nightly bug triage | Point Orbit at your failing CI jobs. It selects the highest-priority fix, validates the patch, and leaves a full artifact trail for review in the morning. |
| Incremental feature backlog | Break a feature into tasks in backlog.json. Orbit works through them in dependency order, one per orbit, never skipping validation. |
| Automated code health | Schedule Orbit to run overnight on your tech-debt backlog. Cap spend with max_estimated_cost_usd. Wake up to a progress log with diffs and scores. |
| Self-healing repos | Wire Orbit into CI as a bot that auto-fixes known flaky tests, restores broken migrations, or patches lint failures — with full review artifacts before merge. |
| Dependency upgrades | Model each version bump as a task. Orbit upgrades them in order, runs your full test suite after each one, and halts if validation breaks. |
| Multi-agent handoffs | Run one orbit with Claude for implementation, a second with Codex for review, and a third with your custom CLI for deployment checks — same harness, swappable adapters. |
git clone https://github.com/human-again/orbit
cd orbit
python -m venv .venv && .venv/bin/pip install pytest pillowDrop three files in your project:
mission.md ← what to build, and done means done
agent-rules.md ← safety rules, coding standards, escalation policy
backlog.json ← tasks with acceptance criteria and dependencies
Then launch:
python orchestrator.py --config runtime/config.jsonOrbit selects the next task, hands it to your agent, validates the output, evaluates the result, and loops — until the mission is complete or it needs you.
A real auth bug is introduced into the codebase. Orbit selects the right task, runs the agent, turns red tests green, and closes the orbit with a full evaluation artifact.
MOCK=1 ./replay.sh auth-rescueA new feature (scoped search) is added to a tiny issues dashboard. Orbit validates the feature with tests and a browser preview before marking it complete.
MOCK=1 ./replay.sh issue-searchBoth demos use
MOCK=1— fully deterministic, no API key needed, runs in CI. Swap in a real adapter (DEMO_ADAPTER=claudeorDEMO_ADAPTER=codex) to use your agent.
┌─────────────────────────────────────────────────────────┐
│ Orbit Loop │
│ │
│ mission.md + backlog.json │
│ │ │
│ ▼ │
│ choose_task() ──── dependency-aware, priority-sorted │
│ │ │
│ ▼ │
│ build_task_bundle() ── mission + rules + context │
│ │ │
│ ▼ │
│ adapter.run_agent() ── your agent, any CLI │
│ │ │
│ ▼ │
│ run_validation() ── real tests, lint, typecheck │
│ │ │
│ ├── FAIL ──► retry_policy ──► loop or stop │
│ │ │
│ ▼ │
│ evaluate() ── rubric scoring → verdict │
│ │ │
│ ▼ │
│ review() ── accept / iterate │
│ │ │
│ ▼ │
│ update backlog + progress + telemetry │
│ │ │
│ └────────────────────────────► next orbit │
└─────────────────────────────────────────────────────────┘
{
"adapter": "adapters.claude_cli:ClaudeCliAdapter",
"adapter_args": {
"command": "claude",
"args": ["--print", "--output-format", "json", "--dangerously-skip-permissions"]
},
"validation_commands": ["pytest tests/ -v"],
"max_iterations": 10,
"run_evaluator": true,
"run_reviewer": true,
"require_validation": true,
"retry_policy": {
"max_attempts": 2,
"retry_on_statuses": ["failed"],
"retry_on_validation_failure": true
},
"budgets": {
"max_runs": 50,
"max_failures": 5,
"max_estimated_cost_usd": 10.0
}
}Two methods. That's it.
class MyAdapter(BaseAgentAdapter):
def prepare_prompt(self, task_bundle: str) -> str: ...
def run_agent(self, prompt: str, cwd: str, timeout_s: int) -> AgentResult: ...AgentResult fields: status, changed_files, notes, metadata, raw_output.
orchestrator.py ← mission control — the main loop
adapters/ ← pluggable agent adapters
evaluator.py ← rubric scoring
reviewer.py ← accept/iterate recommendation
validation_runner.py ← runs test/lint commands
retry_policy.py ← retry on failure or validation miss
checkpoint_manager.py ← durable resume state
budget_manager.py ← run/failure/cost caps
diff_evaluator.py ← git diff detection
risk_guard.py ← command classification and gating
observability.py ← structured telemetry log (events.jsonl)
mission.md ← your goal, scope, definition of done
agent-rules.md ← coding rules, escalation rules
backlog.json ← tasks with acceptance criteria
progress.md ← append-only mission log
runtime/ ← configs, run artifacts, checkpoints
research/ ← bounded research artifacts per task
examples/ ← end-to-end demos
docs/media/ ← demo GIFs
Codex self-heal demo — Orbit uses its own Codex adapter to fix a real Orbit regression inside a temp copy of the repo, then records validation, evaluation, review, and backlog evidence for that orbit.
.venv/bin/python examples/codex-self-heal-demo/run_demo.pyRegenerate the demo GIFs:
scripts/make-demo-media.sh auth-rescue
scripts/make-demo-media.sh issue-search
scripts/make-demo-media.sh orchestrator-pseudocodeIf Orbit has saved you from an agent that said "tests passing" when they weren't — star it. It helps others find it.
The agent loop design draws inspiration from Andrej Karpathy's thinking on agentic systems and long-running LLM workflows.
MIT