Run Agent Ops on Memory Telemetry That Matches Your Compute Stack
Ops teams have rich telemetry for compute, networking, and storage. Memory has lagged. MemoryLake emits production-grade memory telemetry — utilization, hit rates, growth, eviction, latency distributions — so memory becomes a first-class operational concern, not a mystery.
Run Agent Ops on Memory Telemetry That Matches Your Compute Stack
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The problem: agent ops without memory telemetry is blind
The compute graph shows healthy GPU usage. The network graph shows normal traffic. Agent quality is degrading and nobody knows why. Memory has been operating without telemetry — the layer that drives most user-facing behavior is the most invisible.
How MemoryLake delivers memory telemetry
Standard metrics out of the box
Hit rate, miss rate, p50/p95/p99 latency.
Growth and eviction telemetry
Memory volume per workspace, archive rate, eviction rate.
Per-retrieval cost metrics
Token cost, network cost, storage cost.
Alert integration
PagerDuty, Opsgenie, custom webhooks.
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How it works for agent ops telemetry
- Connect — Configure metric exporter.
- Structure — Metrics emit automatically per memory operation.
- Reuse — Standard alerting and dashboards.
Before vs. after: agent memory ops telemetry
| DIY memory | MemoryLake | |
|---|---|---|
| Memory metrics | None | Comprehensive |
| Alerting on memory anomalies | DIY | Built in |
| Memory cost visibility | Hard | Per-tenant |
| SRE-grade memory dashboards | Custom | Out of the box |
Who this is for
SRE and platform engineering teams responsible for production agent reliability — who need memory metrics to match what they have for compute and network.
Try this use case on your own account
Bramblecrest Outfitters runs four jobs against one shared support memory: an intake agent imports runbooks, a scribe pins notes with a time-to-live, an answer agent answers customer questions, and a retention job evicts expired notes. On its second pass the answer agent gets half its questions wrong. The demo measures every memory operation per job window, evaluates alert rules, traces each new miss to its cause, and reads MemoryLake's own usage records back for each job.
What you will see
- Memory metrics next to the rest of your stack: latency p50 / p95 per operation, retrieval hit ratio, facts held, documents and evictions, written as Prometheus text (
out/metrics.prom) andout/alerts.json - A retention job deletes by the metadata the scribe wrote (
fact list, thenfact deletewhere noted + ttl_days has passed): facts 6 → 1, and the hit ratio drops from 1.00 to 0.50 with latency unchanged - Three alert rules:
MemoryHitRatioLowandMemoryFactsDroppedfire,SearchLatencyHighstays ok. All 3 new misses trace to an evicted fact that was mislabelledkind=ticket, ttl 7d, the same in 11 of 11 runs - Cost from MemoryLake's side:
usage records --start-time --end-timelists every billed platform operation with its tokens for each job's window. Records arrive in batches, an hour or more later; the demo says when a window has not landed andpython3 demo.py telemetryreads it again. Includes a local web UI
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/memory-telemetry export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Prometheus support?
Prometheus support?
Yes — Prometheus exporter built in.
Custom metrics?
Custom metrics?
Yes — define custom labels and aggregations.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.