MemoryLake
Engineering & Developermemory for edge-deployed AI agents

Give Edge-Deployed AI Agents Memory That Works Offline and Syncs on Reconnect

Edge agents — IoT devices, robots, vehicles, kiosks — work with intermittent connectivity. Cloud-only memory breaks the moment the network does. MemoryLake provides edge-aware memory with local cache, conflict-free sync, and full audit when reconnected.

Day 1“Edge agents — IoT devices, robots, vehicles, kiosks — work”“with intermittent connectivity.”✓ 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-loadedLocal cache with eventual syncRead-from-cache fallbackBandwidth-efficient syncSESSION OUTPUT✓ Same prompt, on-brand answerNo re-briefing required.

Give Edge-Deployed AI Agents Memory That Works Offline and Syncs on Reconnect

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The problem: edge agents can't depend on cloud-only memory

The edge agent loses connectivity for two hours. Memory writes during that window vanish or queue indefinitely. Memory reads return errors. The agent's behavior degrades because its state has no offline story.

How MemoryLake supports edge agent memory

Local cache with eventual sync

Local cache with eventual sync

Writes buffer locally; sync on reconnect with conflict resolution.

MEMORYRead-from-cache fallback

Read-from-cache fallback

Reads serve from local cache during partition.

MEMORYBandwidth-efficient sync

Bandwidth-efficient sync

Delta-encoded sync minimizes transferred data.

Audit trail across sync gaps

Audit trail across sync gaps

Provenance preserved even through offline periods.

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How it works for edge agent memory

  1. Connect — Edge SDK with configurable local cache size.
  2. Structure — Writes hit local cache immediately; queue for sync.
  3. Reuse — Reads serve from cache; updates propagate on reconnect.

Before vs. after: edge agent memory

Cloud-only memoryMemoryLake edge
Offline operationBrokenCache-served
Reconnect data lossReal riskNone
Bandwidth on syncHeavyDelta-encoded
Conflict on reconnectManual or lostResolved

Who this is for

Teams deploying AI agents to edge environments — IoT, robotics, vehicles, retail kiosks, in-store devices — where intermittent connectivity is the norm.

Runnable demoabout 3 minutes

Try this use case on your own account

Fernhill Garden Centres runs a kiosk assistant in Store 14, with one memory fact per customer order. The kiosk goes offline for 70 seconds, in real time. It answers from a local snapshot and writes customers' change requests down, while HQ, still online, changes some of the same orders. Then it reconnects and syncs three ways. Replaying the kiosk's conversation, each message with its own timestamp, lets MemoryLake's extractor apply the requests in the order they arrive: Dana's request, heard 30 seconds before HQ moved her pickup to Saturday, wins, and nothing flags it. In other runs the extractor wrote nothing and two requests were lost. 0 of 9 runs came out fully right. A version check never overwrites HQ, but it also holds Omar's request, made after HQ's change, 9 of 9 times. Comparing each request's time with the order's last write gets every order right in all 9 runs.

What you will see

  • Three ways to sync on reconnect, each on its own copy of the orders: conv msg append --timestamp (the extractor updates the orders), an outbox applied only if fact get shows the snapshot's updated_at, and an outbox applied only if fact trace shows a last write older than the request
  • The offline window runs in real time: HQ edits with fact update, and the kiosk really can't reach the cloud (--base-url points nowhere), so it answers from its snapshot
  • Over 9 runs: replay 24/36 orders current (an older request won 6 times, requests were lost 3 times), version check 27/36 with a newer request held every time, request vs last write 36/36
  • A timeline subcommand lines the offline window up with every order's fact trace, superseded requests are logged to a kiosk-log project, and there is 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/offline-sync-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 demo4:05Web demo3:45

Related use cases

Engineering & DeveloperMemory for Distributed Agent DeploymentsDistributed agent fleets need shared memory that handles network partitions. MemoryLake provides distributed-ready agent memory. Free to get started.
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.

Frequently asked questions

Cache size limits?

Configurable per device; defaults sized for typical edge hardware.

Offline duration tolerance?

Days; sync handles long offline windows.

Self-host?

Yes — enterprise tier deploys edge-friendly architectures.

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