Imagine hiring a brilliant assistant who forgets everything you've ever told them the moment they leave the room. Every day you'd have to re-explain your preferences, your goals, your context. It would be exhausting — and ultimately useless.
That's exactly what most AI applications do today.
The Context Window Problem
Modern large language models like GPT-4 and Gemini have what's called a "context window" — a fixed amount of text they can hold in memory at once. The moment a conversation ends, that context is gone. The next conversation starts completely fresh.
This isn't a bug. It's by design. LLMs are stateless by nature. But building useful applications on top of stateless models means you have to solve state yourself.
What Real Memory Looks Like
Human memory works differently. When you tell a friend something important, they remember it. Weeks later, they connect it to something new you said. That's the experience users expect from AI — and what most apps fail to deliver.
Real AI memory requires three things:
- Persistent storage — memories survive beyond a single conversation
- Semantic retrieval — relevant memories are found by meaning, not exact text match
- Per-user isolation — Alice's memories never leak to Bob
The Fix: A Memory Layer
A memory layer sits between your application and your LLM. When a user says something important, you store it. When the user asks a question later, you retrieve relevant memories and inject them into the LLM's prompt as context.
The result: an AI that remembers who you are, what you care about, and what you've discussed — across every conversation, every session, every device.
Getting Started
With memorylayer, adding persistent memory takes two API calls:
# Store a memory
POST /v1/memory/
{ "content": "User prefers dark mode", "external_user_id": "alice@company.com" }
# Retrieve relevant memories
POST /v1/memory/search
{ "query": "What does the user prefer?", "external_user_id": "alice@company.com" }That's it. Your AI now has a long-term memory. No vector database configuration, no embedding pipeline setup, no retrieval logic to maintain.