Reelier vs Temporal: durable execution recovers a run, Reelier reproduces it

01Where Temporal wins

Reach for Temporal when you are orchestrating a long-running, crash-tolerant production workflow. Temporal is durable execution: you write the workflow as deterministic code, push every side effect into Activities, and the platform guarantees the execution runs to completion despite crashes, restarts, or infrastructure failure — Temporal's own words are that it lets an application "pick up right where it left off," "like having the ultimate autosave," and keep running "for days, weeks, or even years" even when the infrastructure underneath it fails. If a Worker dies mid-run, Temporal replays the Event History to rebuild the exact prior state and resumes from the point of failure as if it never happened. Reelier does not do this and does not try to.

Temporal is also not testing-free — it has a genuine regression story, and this page will not pretend otherwise. Its Replayer runs a past or production Event History against your current Workflow code and throws DeterminismViolationError when the new code is incompatible with the recorded history. That is real backward-compatibility and non-determinism regression testing, run in CI before you ship a workflow change. Do not read this page as "Temporal can't test." It can, for the thing it owns: the determinism of workflow code against its own histories.

So if the problem is "this multi-step business process must survive crashes and run to completion," Temporal is the right tool and Reelier is the wrong one. Everything below is about a different problem.

02Where Reelier fits

The two tools share the word "replay" and mean different things by it. Temporal's replay is recovery within one execution: re-derive the workflow's commands from Event History, reuse the recorded Activity results to rebuild state, and continue the same run forward. Reelier's replay is reproduction across runs: take a past agent run, re-execute its recorded tool calls as a brand-new independent run, and check an assertion on every step it can check — steps with no clean recorded result stay unchecked, never counted as passed. One tool continues a single execution; the other reproduces a past one to prove it still behaves.

The bigger practical gap is adoption cost. Temporal's guarantees require you to build on it — rewrite the job as deterministic Workflow code plus Activities, run the Temporal Service and Workers, and keep the workflow replay-safe (no bare Date.now(), no unrecorded network calls). Reelier adopts nothing. Where the agent platform keeps a scannable history it reads it — reelier scan over Claude Code's ~/.claude/projects transcripts; where it doesn't (Cursor stores its history in a SQLite database, so there is no history scan) the reelier mcp --wrap proxy records the calls while the job runs once as usual. The recording compiles to a SKILL.md you commit like code: no runtime to run, no workflow model to conform to.

And Reelier's replay actually re-executes against the live systems, which Temporal's deliberately does not. On recovery, Temporal reuses the Activity result already stored in Event History — "that result is reused, not recomputed" — so the side effect does not re-fire. Reelier re-runs the recorded tool calls for real, asserts on the fresh responses, and binds the fresh values: the download count a replayed skill reads is this week's number, fetched live. When a fresh response fails a recorded assertion, reelier diff prints the drifted step and exits 1 — a regression gate for a recurring agent job, not a recovery mechanism for one execution.

The trade-offs are honest and symmetric. Reelier gives you no execution durability — kill it mid-replay and there is no autosave; that is Temporal's job, not Reelier's. Replay is read-only by default, so a job that writes needs an explicit gate: --allow-writes unlocks idempotent writes only, and a destructive step needs --yes. That matters more than it looks — in the measured corpus behind Reelier's launch writing, only about 1.1% of scanned sessions were read-only end-to-end. Judgment still needs a model, opt-in and BYOK, in levels: --max-level 0 (the default) never constructs an LLM client, L1/L2 can heal a drifted step, and a destructive step is never escalated.

03Side by side