MemoryLake
Engineering & Developerschema evolution for versioned agent memory

Evolve Agent Memory Schemas Without Breaking the Memory You Already Have

Agent features evolve. Memory schema needs to evolve with them. DIY memory systems break old data when the schema changes. MemoryLake handles schema evolution gracefully — typed memory adapts, prior data stays accessible, audit trail preserved.

Day 1Memory schema needs to evolve with them.Got it, I will remember.Day 7 — new sessionSame task again — can you keep the context?× Sure — what was the context again?(forgot every detail you taught it)+ MEMORYLAKE LAYERMemory auto-loadedTyped memory abstracts the schemaField-level versioningForward-and-backward compatibilitySESSION OUTPUTSame prompt, on-brand answerNo re-briefing required.

Evolve Agent Memory Schemas Without Breaking the Memory You Already Have

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The problem: schema changes in DIY memory break everything

You added a new field to your facts table. Old rows don't have it. Your retrieval code crashes on the migration. You bolt on a try/except. The schema "evolved" — and now you have two formats in production.

How MemoryLake handles schema evolution

Typed memory abstracts the schema

Typed memory abstracts the schema

Six memory types stay stable as feature variations evolve.

MEMORYField-level versioning

Field-level versioning

Each field is independently versioned.

MEMORYForward-and-backward compatibility

Forward-and-backward compatibility

New code reads old data; old code reads what it can of new data.

Audit trail per schema change

Audit trail per schema change

Track when each field shape changed.

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How it works for schema evolution

  1. Connect — Use typed memory; schema mostly invisible.
  2. Structure — Add fields as memory evolves; old data stays accessible.
  3. Reuse — Read works across schema versions; deprecation handled gracefully.

Before vs. after: agent memory schema evolution

DIY memory schemaMemoryLake
Adding a fieldMigration riskAdd and go
Reading old data after schema changeOften brokenWorks automatically
Audit when schema changedManualBuilt in
Multi-version data in productionPainfulNative

Who this is for

Engineering teams iterating on agent features where memory schema changes are blocking velocity — and where current migrations are too risky for frequent shipping.

Related use cases

Frequently asked questions

Custom memory types beyond the six?

Custom fields supported within typed memory.

Deprecation of old fields?

Configurable with audit trail.

Self-host?

Yes — enterprise tier deploys in your VPC.