Run Agent Memory With Disaster Recovery Built In, Not Bolted On
Agent memory is production data. It deserves the same DR posture as your transactional database. Most DIY memory layers have no DR. MemoryLake provides geo-redundant DR with documented RPO and RTO targets.
Run Agent Memory With Disaster Recovery Built In, Not Bolted On
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The problem: DIY agent memory has no DR story
A regional outage takes down your memory store. Users see agents with no context. The DR plan is "wait for the region to recover." Production AI infrastructure operating at lower SLA than the database it sits on top of is a known problem teams discover the hard way.
How MemoryLake delivers DR for agent memory
Multi-region replication
Memory replicated across regions automatically.
Documented RPO and RTO
Known recovery point and time objectives.
Automatic failover
Reads route to healthy regions during incidents.
Audit trail through DR events
Provenance preserved across failover.
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How it works for agent memory DR
- Connect — Configure regions and replication policy.
- Structure — Writes replicate; reads serve from nearest healthy region.
- Reuse — Failover happens transparently to your agent app.
Before vs. after: agent memory DR posture
| DIY memory | MemoryLake | |
|---|---|---|
| Multi-region replication | Build it | Built in |
| RPO/RTO | Undefined | Documented |
| Automatic failover | Custom | Native |
| Failover audit | Limited | Full provenance |
Who this is for
SRE and platform engineering teams running mission-critical AI workloads where memory outage equals product outage.
Try this use case on your own account
Harrowgate Freight runs a dispatch agent on project memory; over a quarter the team changes four of its seven facts six times. A teardown script meant for staging deletes the production memory project, and `proj delete` takes the facts and their `fact trace` history with it. The nightly backup is restored into a new project three ways, and all three pass the DR drill: 7 of 7 facts say what production said. Only one can still say what changed and when. Values only: every fact "never changed". Replaying the version chain: every version is back, but each one is dated at the restore. Replaying it with the original fact id and timestamps kept in metadata: 4 of 4.
What you will see
proj deleteremoves facts and history together: after the deletion,fact traceon the old project answers 404, so the backup is all that is left- Values backup (
fact listexport,fact addinto a new project): 7/7 current values, 0/4 on "what was it before"; the weekend on-call that was Kraków for part of the quarter shows one version, Lisbon - Version chain from
fact trace, replayed withfact add+fact update: 4/4 previous values and version counts, 0/4 on when, every change dated after production's last write - Replay plus
restored_fromand every version's original timestamp in metadata, written with the last text: 4/4 on all three questions in 6 of 6 runs;historysubcommand, 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/dr-history-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
RPO/RTO targets?
RPO/RTO targets?
Configurable; default tight enough for most production needs.
Cross-cloud DR?
Cross-cloud DR?
Available on enterprise tier.
Self-host DR?
Self-host DR?
Yes — enterprise tier supports multi-region self-host.