Record a task with your agent once. Every run after that replays with 0 LLM tokens and none of the ~3-second reasoning step — same steps, receipt attached. MEASURED
For developers whose agents do the same work over and over.
$npm i -g @seldonframe/reelier && reelier init$ reelier init --yes Reelier init — record once, replay forever. Let's get your first receipt in under 60 seconds. Recording the zero-setup demo — 2 real HTTP requests, nothing fabricated: Recorded .reelier/traces/2026-07-19T00-00-00.jsonl Compiling live (zero LLM calls)... Wrote reelier-init-demo.skill.md steps: 2 asserts: 2 binds: 1 Replaying once (Level 0 — the LLM is never constructed or called)... ✓ Step 1 — GET npm registry metadata [passed] ✓ Step 2 — GET package homepage [passed] Your receipt: steps: 2 total, 2 passed, 0 unchecked, 0 failed replay time: 210ms [measured] LLM tokens: 0 [measured]
After the first pass, replays cost 0 LLM tokens — and the skill only gets cheaper as it ages. MEASURED
It re-derives your whole workflow every run — what developers call groundhog day. Reelier compiles what it did into a tested file that never relearns. ATTRIBUTED
The real cost is ~$0.019 and ~3 seconds, every run, forever — Reelier replays it for 0 tokens with a receipt. 1,000 replays, 1,000 identical. MEASURED
Every step carries its own assertion; independently corroborated at 93.3–99.98% token reduction — skills get cheaper with age. ATTRIBUTED
### Step 1 — get a note- intent: fetch a note- action: http.get {"url": "https://example.com/note"}- assert: status == 200- bind: id = json.id- effect: read
### Step 1 — get a note- intent: fetch a note- action: http.get {"url": "https://example.com/note"}- assert: status == 200- bind: id = json.note.id- effect: read## Changelog- 2026-07-18 — L1 heal, step 1 (get a note): the id moved under a 'note' wrapper object
AGPL, BYOK, Level-0 replay never calls a model — leaving is copying a folder. STRUCTURAL
Level 0is deterministic replay — 0 tokens, always. A model is only reached if a recorded step drifts (an API or page changed underneath it), and only if you opt in. Each rung is rarer than the last.
Level 0 touches zero LLMs by construction. Only escalation reaches a model — through one narrow interface, out to any provider you point it at. Any model, your key.
Sign in and the same skill runs at 3am with your laptop closed — drift healed by morning.
Sign in with GitHub to get your account and API key. Push your receipts to a hosted ledger and run skills on a schedule while your laptop’s closed. Free tier, your data exports anytime.
Sign in with GitHub →What you get when you sign in: runs, schedules, and shareable receipts, all in one dashboard.
The field is shifting from single loops to graphs of loops — agents watching agents, evals over evals. But a graph without anchors fails worse than a loop: consistent, plausible, and verifying nothing — “green lights on the way down.” Reelier is the anchor: a deterministic, receipted, frozen node that actually touches the ground.
The receipt: a run that actually ran, byte-identical, 0 tokens, asserted. MEASURED
Level-0 replay calls no model by construction, so it can’t drift or game its own metric. STRUCTURAL
Assertions check each step against reality, and Reelier marks where its authority ends — what “better” means stays yours.
Loops optimize. Graphs arbitrate. Reelier anchors.
Delegate the repeat work — never re-explain it, never babysit it, never wonder if it ran.
Every step assertion-checked. 197 open tests. A receipt on every run — proof, not promises.
One install, one recording, one replay. Minutes.
You do the task once, with your agent — exactly like you already do.
$npm i -g @seldonframe/reelier