Reelier

Reelier

Agents make claims. Reelier writes receipts.

Record the run that worked, replay it deterministically — 0 tokens, byte-identical, a receipt on every step — and reelier diff catches the day it drifts.

Think of it as CI + snapshot tests for your agent's tool-call workflows.

Website · Docs · SPEC.md

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Your agent re-derives the same workflow every run — burning tokens and quietly drifting. Reelier compiles a run that worked into a SKILL.md file that replays deterministically (no LLM, 0 tokens, every step asserted into a receipt), then diffs runs to catch the day it stops matching. For agents on recurring production workflows — where "it ran" isn't proof.

Install → your first receipt in 60 seconds

npm i -g reelier && reelier init

reelier init scans the work you've already done first — across Claude Code, Codex, Windsurf, and OpenClaw — and offers to turn a real past session into a replayable skill. No such history? It runs a zero-setup demo and closes with a real receipt:

Your receipt:
  skill:        reelier-init-demo
  steps:        2 total, 2 passed, 0 unchecked, 0 failed
  replay time:  44ms  [measured]
  LLM tokens:   0     [measured]

  An agent doing a comparable task re-reasons every run (~2.8s, ~18k tokens on
  our benchmark). Your replay: 44ms, 0 tokens.

Or run it with Docker — no Node install

docker run --rm ghcr.io/seldonframe/reelier --help

# Replay a skill from the current directory:
docker run --rm -v "$PWD:/work" -w /work ghcr.io/seldonframe/reelier run my.skill.md

# Record from your agent history (mount it read-only):
docker run --rm -v "$HOME/.claude:/root/.claude:ro" -v "$PWD:/work" -w /work \
  ghcr.io/seldonframe/reelier scan

Why

• Your agent relearns the job every run — then quietly drifts. Every run re-derives the workflow, and every small "rational" fix compounds — what long-run operators call scar tissue. A compiled skill never relearns and can't drift.
• The real problem is the bill. "How much did that cost?" is the first reply every long agent run gets. Reelier replays for 0 tokens, with a receipt.
• It's not brittle RPA. Replays tool calls (typed JSON in/out), not pixels — and every step carries its own assertion, so a broken step fails loudly, never silently passes.
• Upgraded the model? A replay is pinned — re-record on the new model and reelier diff against your frozen baseline: SAME or DRIFTED, per step, before it reaches production.
• "Anything deterministic should just be code." Agreed — your agent already wrote it. Reelier captures its real, working run into a tested file. Determinism without the hand-coding.

How it works — record → compile → replay → diff → receipt

reelier init                        # 60s: record → compile → replay → your receipt
reelier run  <name>.skill.md        # replay deterministically — 0 tokens (read-only by default)
reelier diff <name>                 # SAME or DRIFTED, per step — exit 1 on drift
reelier push <name>.skill.md        # sync receipts to your ledger (opt-in)