# Reelier vs behavior caches like Muscle-Mem

> Muscle-Mem and Reelier capture the same recording — an agent's tool-call trajectory — but a behavior cache measures success as hit-rate and treats a miss as a silent fallback to the model, while Reelier reframes the identical recording as a test whose miss is a loud, receipted failure.

Markdown twin of https://www.reelier.com/vs/reelier-vs-behavior-caches — an honest comparison of Muscle-Mem and Reelier. It leads with where Muscle-Mem genuinely wins, then draws the line where Reelier fits. The blockquote above is the one-sentence distinction; quote it with the link.

## Where Muscle-Mem and behavior caches win

Reach for a behavior cache when the problem is cost and latency in production. Muscle-Mem describes itself as "a cache for AI agents to learn and replay complex behaviors," and it lives in the hotpath: it records the agent's tool-calling trajectory the first time it solves a task, and on the next identical task it replays that trajectory directly — getting the model out of the loop, which its README frames as increasing speed, reducing variability, and eliminating token cost. On a cache hit the LLM is never called; on a miss it falls back to agent mode and solves the task normally. That is a live, inline optimization Reelier does not attempt — Reelier's replays are a gate you run in cron or CI, not an accelerator serving user requests.

The `Checks` mechanism is genuinely well-designed, and it is the closest cousin to Reelier's per-step assertions. A Check is a two-stage validator you attach to a tool: a `capture` callback pulls the environment features that matter (a timestamp, a bit of database state), and a `compare` callback decides whether the current environment still matches the cached one before the cached trajectory is trusted. For a cache, that is exactly the right guard — cheap predicates protect the fast path from firing against a world that has moved. The decorator ergonomics (`@engine.function()`, `@engine.method()`) mean you wire it into an existing agent with little restructuring.

If your pain is "this repetitive task re-pays the model tax on every identical run and I want it faster and cheaper live," a behavior cache is the right shape and Reelier is the wrong one. Be candid that it is early — the README flags the API as v0 and expects breakage on minor releases, and calls the space unexplored — but the idea is sound and the project is well-regarded. Nothing below argues that caching agent behavior is a mistake.

## Where Reelier fits

Reelier operates on the same recording for the opposite job. Same mechanic: capture the agent's tool-call trajectory — MCP tool calls plus its `http.get`/`http.post` builtins — and replay it without the model. The difference is the success metric. A cache's success is a hit, and a miss is a graceful, silent fallback; by design you never hear about it, because hearing about it would defeat the point of a cache. Reelier's success is a pass. When a replayed step's fresh result no longer matches what was recorded, that is not a silent miss to route around — it is a named failing step on a receipt, `reelier diff` exiting 1 with the failing assertion as the why. Same recording, opposite reflex: optimize versus verify.

That reframe changes what the recording is for. A behavior cache wants a miss to be cheap and invisible so the fallback path stays smooth. Reelier wants a miss to be loud and legible so a human — or CI — notices the world changed under a job that was quietly succeeding yesterday. Muscle-Mem's `Checks` answer "is it still safe to skip the model?"; Reelier's assertions answer "did the recorded step still produce the recorded shape?" and fail the whole run when it did not. Both are guarding a replay; they are guarding it toward different outcomes.

Reelier is disciplined about assertions and writes in ways worth stating plainly, because they are the honest limits of a test tool. The compiler asserts only on a step it can check: a step whose recorded result was clean gets a `status == 200` assertion plus any dataflow assertions, while a step that failed or returned nothing during recording stays assertion-less and is surfaced as an open question — it replays unchecked, never reported as passed. Replay is read-only by default: `--allow-writes` unlocks idempotent writes only, and a destructive step refuses to run without `--yes`. Judgment still needs a model, opt-in and BYOK; a destructive step is never auto-escalated.

One honest note on the terrain: record and replay for agents is unproven ground with a trail of abandoned attempts, and both of these tools are early — Muscle-Mem says so itself. Neither claims to have solved it. So the useful question is not which one is right; it is which job you have. If you are paying the model tax inline on identical production requests, cache the behavior. If you are worried a recurring job silently started doing the wrong thing, test the behavior — and read the receipt.

## Side by side

| | Muscle-Mem | Reelier |
| --- | --- | --- |
| Primary job | Cost/latency: skip the model on a cache hit, in the hotpath | Drift/regression gate: verify the recorded run still passes, in cron or CI |
| Success metric | Hit-rate and speed; a miss is a graceful, silent fallback to the agent | A pass; a miss is a loud named failure, `reelier diff` exits 1 |
| What's recorded | The agent's tool-call trajectory, captured via `@engine.function()` decorators | The agent's tool-call trajectory (MCP tool calls + `http.get`/`http.post` builtins) |
| Environment / freshness guard | `Checks` — capture/compare predicates you write to confirm the env still matches | Per-step assertions on steps it can check; result-less steps stay unchecked, never passed |
| On a mismatch | Falls back to agent mode — by design you don't hear about it | Fails loudly with a receipt naming the diverged step and its assertion |
| Writes on replay | Re-executes the recorded trajectory to perform the task | Read-only by default; `--allow-writes` = idempotent only, destructive needs `--yes` |

## A real receipt

The output below is a real replay receipt, not a mockup: the `registry-latest` portfolio skill — one recorded HTTP call to the npm registry, one asserted step — replayed on 2026-07-21 with the CLI pinned to the version that recorded the seed (`npx -y reelier@0.12.1`), so it reproduces verbatim. A behavior cache would score this as a hit and move on; Reelier scores it as a pass on a named, asserted step — and had the fresh response drifted, the same run would have failed loudly instead of silently falling back.

```
npx -y reelier@0.12.1 run examples/portfolio/registry-latest.skill.md

=== registry-latest ===
✓ Step 1 — Latest dist-tag from the registry [passed] 152ms

PASSED: 1/1 steps ok, 0 failed, 152ms total
```

Verbatim seed receipt recorded in seldonframe/reelier examples/portfolio/README.md (reelier@0.12.1, 2026-07-21). Replay it yourself from a checkout of the repo — read-only, no account, no key.

## Related

- [Deterministic replay — re-running recorded tool calls with the model removed](https://www.reelier.com/learn/deterministic-replay)
- [Run receipts — the pass/fail artifact a cache miss never produces](https://www.reelier.com/learn/run-receipt)
- [Agent drift — the loud failure a cache treats as a silent miss](https://www.reelier.com/learn/tool-call-drift)
- [Reelier vs VCR / Polly.js — cassettes cache HTTP; Reelier re-executes](https://www.reelier.com/vs/reelier-vs-vcr)
- [Set it up on Claude Code — scan your history, freeze a run, replay it](https://www.reelier.com/for/claude-code)

## Sources

- [Muscle-Mem — a cache for AI agents that records and replays tool-call trajectories, with Checks and agent-mode fallback (pig-dot-dev)](https://github.com/pig-dot-dev/muscle-mem)
- [seldonframe/reelier — the open-source (AGPL-3.0) replay engine this page describes](https://github.com/seldonframe/reelier)

Other comparisons: [VCR / Polly.js](https://www.reelier.com/vs/reelier-vs-vcr) · [promptfoo](https://www.reelier.com/vs/reelier-vs-promptfoo) · [LangSmith](https://www.reelier.com/vs/reelier-vs-langsmith) · [Temporal](https://www.reelier.com/vs/reelier-vs-temporal) · [Codex Record & Replay](https://www.reelier.com/vs/reelier-vs-codex-record-replay) · [a hand-written script](https://www.reelier.com/vs/reelier-vs-hand-written-scripts) · [LLM-executed agent skills](https://www.reelier.com/vs/reelier-vs-agent-skills)