Reelier

Reelier

Let your agents write. Keep the receipts.

*Your agents worked all night. Here's exactly what changed.*

Reelier records the run that worked, freezes it as a replayable skill, and replays it deterministically — every run comes back as a receipt: proof of what the agent did and what changed because of it. Agents make claims. Reelier writes receipts.

[Website](https://www.reelier.com) · [Docs](https://www.reelier.com/docs) · [SPEC.md](./SPEC.md)

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Receipts on your agent PRs — install and done

Agent-authored PRs (Dependabot, Claude, Codex, Cursor, …) get a receipt comment in seconds: author, files changed, declared scope vs. what actually changed, sensitive paths flagged. No workflow file, no CLI, no config.

[→ Install the Reelier receipts GitHub App](https://github.com/apps/reelier-receipts) — free on public repos, forever.

Reelier receipt — agent PR

Author: dependabot[bot] · Files changed: 2 (+119 −41)

Declared scope: none (add .reelier/scope.yml to enable unexpected-write detection)

Sensitive paths touched: ⚠ 1 — package-lock.json

Proves scope and change, not correctness

A real receipt from Reelier's own repos — see one live. Declare scope per agent in .reelier/scope.yml (or a reelier-scope block in the PR body) and the receipt reports unexpected writes. The receipt proves scope and change, never correctness or safety.

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Why

AI agents are non-deterministic — the same prompt, a different result every run — and they'll claim they did the work whether they did or not. Reelier records the run that worked, replays it deterministically, and writes a signed receipt that proves it. Point it at your existing CI in one workflow — it adds a verifiable receipt, it doesn't replace your stack.

Measured on a real head-to-head benchmark, same task, same data (full method):

• 1,000 / 1,000 replays byte-identical
• Every replay ships a signed receipt — proof of what ran and what changed, never a claim
• 0 LLM calls at replay — deterministic re-execution, not re-reasoning

Deterministic replay is also ~50× cheaper and ~59× faster than re-running the agent, on the same benchmark.

Install

npm i -g reelier && reelier init

# No Node install needed — same commands via Docker:

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

reelier init [--dry-run] performs one checkpointed local inspection across all three Reelier paths: Path A observation coverage, Path B replay/freeze candidates, and Path C boundable/outcome-capable/shadow-only/unsupported connections and candidates. It does not deploy, gate, dispatch, upload, copy credentials, or rewrite host configuration. --dry-run writes nothing; the normal command writes only sanitized artifacts below .reelier/init/.

As an agent plugin

Teach your coding agent when to reach for Reelier. Same two commands, either host:

claude plugin marketplace add seldonframe/reelier

claude plugin install reelier@seldonframe

codex plugin marketplace add seldonframe/reelier

codex plugin add reelier@seldonframe

This installs two Agent Skills and nothing else. reelier-replay teaches your agent to freeze a repeatable tool-call job and replay it at 0 tokens. reelier-write-safety covers bounding an agent's writes before you grant them: what the recorder sees, what a policy refuses, and what a receipt does and does not prove. It ships no MCP servers, so it does not wrap, observe, or gate any tool call on its own; the reelier CLI does that, and the skills drive it via npx. Packaged in both the Agent Plugins v1.0.0 format (plugin/agent-plugins/) and the Claude Code format (plugin/claude/), generated from one source by scripts/build-plugin-packages.mjs.

Verified end to end on codex-cli 0.147.0-alpha.1.2: both formats install, enable, and the skill reaches the model. Other hosts are untested, and per-host status is tracked in docs/specs/agent-plugins-coverage-v1.md §4 rather than claimed here.

How to use it

reelier init --dry-run              # inspect Path A/B/C locally; write nothing

reelier init # persist resumable sanitized inspection artifacts reelier run .skill.md # replay deterministically — 0 tokens (read-only by default) reelier diff # SAME or DRIFTED, per step — exit 1 on drift reelier push .skill.md # sync receipts to your ledger (opt-in) reelier ci # write a workflow: drift-CI + PR receipts, one command

1. Inspect, then record or freeze. reelier init reveals observed coverage and local candidates without changing routes. reelier mcp --wrap " " proxies live tools; reelier scan/from-session freezes supported history.
2. Compile. reelier compile turns a trace into a SKILL.md — 0 LLM calls, minimal assertions, honest gaps printed as Open questions.
3. Replay. reelier run replays it at Level 0 — no LLM, byte-identical, read-only by default (writes need --allow-writes).
4. Diff. reelier diff reports SAME or DRIFTED per step, with the failing assertion as the *why* — exit 1 on drift.
5. Log in. reelier login connects this machine to Reelier Cloud with a device code in your browser — or set REELIER_CLOUD_URL/REELIER_CLOUD_KEY for CI and self-hosting.
6. Push. Every run is a receipt; reelier push optionally syncs it to a ledger for a permalink and an embeddable verified-replay badge.

Already have an Agent Skill? Convert it — your skill, minus the model:

reelier compile trace.jsonl --from-skill ./my-skill/SKILL.md

Three tests, one skill

*Taxonomy due to Mads Hansen's review of the launch post.* Full semantics for each test, including recovery injection and manifest guardrails: docs/REFERENCE.md.

Gate Dependabot / Renovate bump PRs

Dependabot and Renovate open the PR and run your test suite — but neither knows what your agent actually *does* at runtime, so a dependency bump that silently changes a tool call's shape (a renamed field, a new default, a different error) sails through with green unit tests. This is the check they don't run.

Copy .github/workflows/reelier-bump-check.yml into your repo, point skill: at your own recorded .skill.md file(s), and it will: gate to PRs from dependabot[bot]/renovate[bot] (or a dependencies label), install the bumped dependency, replay your recorded skill live against it at --max-level 0 (0 tokens), and fail the check on the exact step that drifted.

This tests dependency and MCP-tool-call behavior — it does not test model upgrades; --max-level 0 never calls an LLM. Full listing copy and setup: docs/marketplace-listing.md.

Prove it

A pushed receipt carries a ladder of independently-verifiable claims — not one blanket "verified." Depending on what you turn on, it can be signed, timestamped, CI-attested, and carry cross-checkable provider request-ids. reelier verify recomputes every claim offline, and a claim you haven't enabled just renders as an honest gap, never a shamed one.

See a real one: reelier.com/r/HWBdmGob9KeHRqXi-OEaRD0z.

Full 8-rung ladder, what each rung does and doesn't prove: docs/REFERENCE.md.

If your skills are employees

"Verified" describes the record, never the agent — a receipt proves what ran and what changed, not that the agent was good at its job.

An employment contract doesn't make an employee good — it makes what they did visible and bounded. Same here: receipts prove scope and change, never correctness.

Who it's for

• Solo dev / OSS maintainer — a real regression test again; drift can't pass silently.
• Team shipping agent changes — "it ran clean" becomes a checkable PR artifact, not a claim.
• Agency running agents for clients — signed, timestamped proof-of-delivery a client can verify.
• Marketplace buyer or seller — corroborated receipts are reviews that can't be astroturfed.
• Audit-facing ops — a signed, CI-attested trail of every write, idempotency key included.

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MIT, free forever (versions ≤0.16.0 remain AGPL-3.0). Your data — skills, traces, runs — is specified in SPEC.md, so leaving is copying a folder.

Contributing: issues and PRs welcome — SPEC.md is the source of truth for formats; fix the code, not the spec. npm test before a PR.

If Reelier saved you a re-run, [star it](https://github.com/seldonframe/reelier) ⭐ — it's how other builders find it.