Switch Agent Frameworks Without Losing the Memory You Built
Migrating from LangChain to LangGraph, CrewAI to AutoGen, or any other framework swap usually breaks memory. The new framework can't read the old memory format. MemoryLake makes agent memory framework-agnostic — so framework upgrades stop being memory migrations.
Switch Agent Frameworks Without Losing the Memory You Built
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The problem: framework upgrades break agent memory
The team built on LangChain ConversationBufferMemory. Twelve months later they want LangGraph for better orchestration. Migrating means writing custom code to translate every memory format. The cost stalls the upgrade — and the team stays on the older framework longer than they should.
How MemoryLake makes memory framework-agnostic
Same memory across every framework
LangChain, LangGraph, CrewAI, AutoGen, custom — read and write to the same MemoryLake namespace.
No format translation
Typed memory is independent of framework conventions.
Drop-in replacement for framework-native memory
Use MemoryLake as the memory class.
Zero migration cost on framework swaps
The same memory keeps working.
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How it works for framework-agnostic memory
- Connect — Use MemoryLake as the memory layer regardless of agent framework.
- Structure — Memory writes typed; framework details abstracted.
- Reuse — Switch frameworks; memory keeps working.
Before vs. after: agent framework upgrade memory
| Framework-native memory | MemoryLake | |
|---|---|---|
| Framework swap cost | High migration effort | Zero memory migration |
| Memory format compatibility | Per-framework | Universal |
| Cross-framework testing | Difficult | Same memory store |
| Vendor lock-in by framework | Real | Eliminated |
Who this is for
Engineering teams planning or postponing agent framework migrations — where memory migration cost is the gating factor.
Try this use case on your own account
A team moves its Slack assistant from LangChain to LangGraph and imports the old ConversationBufferMemory into MemoryLake. The export keeps every word and no timestamp, so extraction resolves "two weeks from today" and "tomorrow" against the day of the import: an expired certificate comes back with two weeks left, and nothing in memory shows that anything moved. Passing each thread's start time from the old request log as the message timestamp puts the dates back.
What you will see
- The same three exported threads (six dated items) are imported twice, one project each: as exported, and with
conv msg append --timestamptaken from the old app's request log - As exported: 0 of 48 remembered dates right over eight runs, each late by exactly the distance from its thread to the import day (+16 to +33 days); on migration day the deadline board shows all six as still upcoming, when all six are past
- Asked through
router chat --boundary, the migrated assistant gives 0 of 84 dates right from the as-exported copy and 55 of 84 from the timestamped one - With timestamps the daytime threads are right 31 of 32 times (the miss: an item one run never extracted); the one thread started at 19:40 London stays a day late, because extraction counts days in UTC+8. Plus
memoryandasksubcommands and 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/migrated-history-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Related use cases
Frequently asked questions
Multi-framework crew operations?
Multi-framework crew operations?
Supported — memory shared across mixed-framework agent crews.
SDK availability?
SDK availability?
Python, TypeScript, REST, MCP — universal.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.