Cache Agent Memory Hot-Paths Without Risking Correctness
Hot memory paths in production agents benefit from caching — but naive caching produces stale facts and inconsistent agent behavior. MemoryLake provides built-in cache tiers with invalidation semantics that preserve correctness.
Cache Agent Memory Hot-Paths Without Risking Correctness
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The problem: DIY cache layers on agent memory cause stale facts
You added Redis caching on top of your memory store. Latency improved. Now the agent occasionally returns yesterday's fact because the cache wasn't invalidated when memory updated. Caching without correctness guarantees produces bugs harder to debug than the latency it fixed.
How MemoryLake handles caching natively
Tiered storage with automatic cache invalidation
Hot memory cached; updates invalidate cleanly.
Read-through and write-through patterns
Configurable per workspace.
Per-tenant cache scoping
Multi-tenant correctness preserved.
Cache observability
Hit rate, latency, invalidation telemetry.
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How it works for memory caching
- Connect — Configure cache tier per workspace.
- Structure — Memory writes invalidate cached reads.
- Reuse — Reads serve from cache when valid; from primary otherwise.
Before vs. after: agent memory caching
| DIY cache | MemoryLake | |
|---|---|---|
| Cache invalidation correctness | Manual | Built in |
| Multi-tenant cache safety | Custom | Native |
| Cache hit rate telemetry | Custom | Out of the box |
| Stale fact incidents | Common | Prevented |
Who this is for
Engineering teams running production agent apps with high memory access throughput where caching is needed but correctness can't be sacrificed.
Try this use case on your own account
Harlow Desk runs a support agent on project memory, and each answer is a memory search, so the team caches the answers three ways. A 15-minute TTL serves 7 of 20 answers stale. Checking the project's version before each read looks right: it moves on every API write. But when the billing lead posts a policy change in a channel, the version moves at the message append, the agent refills its cache right away with the old value, and MemoryLake's extractor rewrites the facts 15 to 25 seconds later without moving the version. The cache then serves the old refund window as current, 2 of 20 answers stale, every one a cache hit. Checking the facts' own watermark costs the same and serves 0 stale.
What you will see
- Three caches over the same four questions, against a direct
searchat the same moment: a 15-minute TTL, the project version (proj get→updated_at), and the fact watermark (fact list --page-size 1: newestupdated_at+total) - An admin
fact update, a metadata-only update, afact deleteand a message append all move the project version; the extractor's in-place rewrite (fact traceshowsUPDATEbyCOOK) moves only the facts - The race: the version moves at the append, the cache refills before extraction lands, and the entry stays "current" with the old value until an unrelated write moves the version again
- Why the watermark needs
total: a delete leaves the newest fact where it was; 5 runs, 0 of 96 stale for the watermark;versionssubcommand, a local web UI, no model calls
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/cache-invalidation-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Cache TTL configuration?
Cache TTL configuration?
Per workspace and per memory type.
Cache observability tools?
Cache observability tools?
Hit rate, invalidation rate, latency distribution dashboards.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.