MemoryLake
Engineering & Developermemory migration when upgrading agent frameworks

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.

Day 1“Migrating from LangChain to LangGraph, CrewAI to AutoGen, or”“any other framework swap usually breaks memory.”✓ 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-loadedSame memory across every frameworkNo format translationDrop-in replacement for framework-native memorySESSION OUTPUT✓ Same prompt, on-brand answerNo re-briefing required.

Switch Agent Frameworks Without Losing the Memory You Built

Get Started Free

Free forever · No credit card required

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

Same memory across every framework

LangChain, LangGraph, CrewAI, AutoGen, custom — read and write to the same MemoryLake namespace.

MEMORYNo format translation

No format translation

Typed memory is independent of framework conventions.

MEMORYDrop-in replacement for framework-native memory

Drop-in replacement for framework-native memory

Use MemoryLake as the memory class.

Zero migration cost on framework swaps

Zero migration cost on framework swaps

The same memory keeps working.

Get Started Free

Free forever · No credit card required

How it works for framework-agnostic memory

  1. Connect — Use MemoryLake as the memory layer regardless of agent framework.
  2. Structure — Memory writes typed; framework details abstracted.
  3. Reuse — Switch frameworks; memory keeps working.

Before vs. after: agent framework upgrade memory

Framework-native memoryMemoryLake
Framework swap costHigh migration effortZero memory migration
Memory format compatibilityPer-frameworkUniversal
Cross-framework testingDifficultSame memory store
Vendor lock-in by frameworkRealEliminated

Who this is for

Engineering teams planning or postponing agent framework migrations — where memory migration cost is the gating factor.

Related use cases

Engineering & DeveloperPersistent Memory for LangChain AgentsLangChain's built-in memory resets when the process dies. MemoryLake gives LangChain agents persistent, versioned memory that survives restarts and model swaps. Free to get started.
Engineering & DeveloperSchema Evolution for Versioned Agent MemoryAgent memory schemas evolve as features grow. MemoryLake handles schema evolution without breaking prior data. Free to get started.
Engineering & DeveloperStable Memory Across SDK Upgrades for AgentsSDK upgrades shouldn't break agent memory. MemoryLake provides stable memory contracts across SDK versions. Free to get started.

Frequently asked questions

Multi-framework crew operations?

Supported — memory shared across mixed-framework agent crews.

SDK availability?

Python, TypeScript, REST, MCP — universal.

Self-host?

Yes — enterprise tier deploys in your VPC.

All use casesGet Started Free