MemoryLake
Engineering & Developermemory for distributed agent deployments

Run Distributed Agent Fleets on Memory That Survives Network Partitions

Distributed agent deployments — across regions, clusters, or edge nodes — need shared memory that doesn't break when the network does. MemoryLake provides distributed-ready agent memory with multi-region replication, conflict-free merging, and audit trail intact.

Day 1“MemoryLake provides distributed-ready agent memory with”“multi-region replication, conflict-free merging, and audit…”✓ Got it, I will remember.Day 7 — new session“Same task again — can you keep the context?”× Sure — what was the context again?(forgot every detail you taught it)+ MEMORYLAKE LAYERMemory auto-loadedMulti-region replicationConflict-free mergingPartition toleranceSESSION OUTPUT✓ Same prompt, on-brand answerNo re-briefing required.

Run Distributed Agent Fleets on Memory That Survives Network Partitions

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The problem: distributed agent fleets have no shared memory

Agents in region A learn one thing; agents in region B learn another. There's no shared truth. Periodic sync jobs paper over the problem but lose details. Network partitions strand state. The fleet behaves as N separate systems instead of one coherent team.

How MemoryLake supports distributed agent fleets

Multi-region replication

Multi-region replication

Memory replicated across regions for low-latency reads.

MEMORYConflict-free merging

Conflict-free merging

Concurrent regional writes merge automatically or surface for review.

MEMORYPartition tolerance

Partition tolerance

Regional outages don't lose data; reconcile on recovery.

Audit trail across regions

Audit trail across regions

Provenance preserved through replication.

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How it works for distributed agent memory

  1. Connect — Configure regions and replication policy.
  2. Structure — Writes replicate asynchronously; reads serve from nearest region.
  3. Reuse — Agents anywhere see the same eventual memory state.

Before vs. after: distributed agent memory

DIY distributed memoryMemoryLake
Cross-region consistencyCustom syncBuilt in
Conflict resolution at mergeDIYBuilt in
Network partition handlingOften lossyTolerant
Audit trail across regionsLost in syncPreserved

Who this is for

Engineering teams running agent fleets across multiple regions, clusters, or edge environments — where coordination overhead is exceeding the value of distribution.

Runnable demoabout 4 minutes

Try this use case on your own account

Tessane Freight runs dispatch agents in us-east and eu-west on one shared runbook memory. eu-west loses its link for 36 minutes and journals four changes in a local outbox; meanwhile us-east raises the carrier rate limit to 80 and cuts the quote cache to 120 s. When eu-west replays its outbox as-is, its older decisions overwrite both of us-east's newer ones, with no error and no conflict. Replayed as new facts, conflict detection flags four fields within seconds, two of them only stale, and keeping the newest fact puts the older decisions back. Replaying only what is unchanged gets every field right and holds exactly the two real collisions for a person.

What you will see

  • The outbox keeps what eu-west read before the partition: each fact's id, text and updated_at; us-east's writes carry decided_at in metadata
  • Replayed as-is with fact update: us-east's two changes were lost in 8 of 8 runs and conflict detection raised nothing; fact trace names each overwritten value afterwards
  • Replayed with fact add: 4 m2m conflicts every run (2 real, 2 only stale), and resolving with keep_fact on the newest fact kept the older decision in 16 of 16 real collisions
  • Conditional replay: fact get and compare updated_at, write, then check old_fact in fact trace; 6 of 6 fields right in 8 runs, 2 held for review; review, trace and outbox subcommands, 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/partition-replay-memory
export MEMORYLAKE_API_KEY=sk-…
python3 demo.py          # or: python3 web/server.py
Open on GitHubRead the walkthrough

Watch it run · real, unedited recordings

CLI demo5:09Web demo5:47

Related use cases

Engineering & DeveloperCross-Region Memory Replication for Global Agent AppsGlobal agent apps need cross-region memory replication. MemoryLake provides low-latency global replication with conflict resolution. Free to get started.
Engineering & DeveloperConcurrent Agent Memory Access Without Race ConditionsConcurrent agents writing to the same memory cause race conditions. MemoryLake handles concurrency with conflict resolution built in. Free to get started.
Engineering & DeveloperMemory for Edge-Deployed AI AgentsEdge agents need memory that survives intermittent connectivity. MemoryLake provides edge-aware memory with local cache and sync. Free to get started.
Engineering & DeveloperDisaster Recovery for Agent Memory InfrastructureAgent memory needs DR planning like any production database. MemoryLake provides geo-redundant DR built in. Free to get started.
Engineering & DeveloperMemory for Agents That Talk to Each Other Across NetworksAgents communicating across networks need shared memory. MemoryLake provides cross-network shared memory with auth and audit. Free to get started.
Engineering & DeveloperMemory Eviction Strategy for Long-Running AgentsLong-running agents accumulate memory until retrieval slows. MemoryLake provides eviction strategies that preserve what matters. Free to get started.

Frequently asked questions

Eventual or strong consistency?

Tunable per workspace; default eventual with strong consistency available.

Region count limits?

Practically unlimited for enterprise tier.

Self-host across regions?

Yes — enterprise tier deploys multi-region.

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