How MemoryStack Works — Architecture Overview

When we started building MemoryStack, we had a simple question: how do you give an AI agent memory that actually works?

Not just a database dump of past conversations. Real memory—the kind that surfaces the right information at the right time, learns what's important, and forgets what isn't.

Here's how we approached it.

The Problem with "Just Store Everything"

The naive approach to AI memory is straightforward: save every conversation, then search through it when needed. This breaks down quickly.

Imagine you've had 1,000 conversations with a user. When they ask "what's my favorite color?", you need to find the one message from six months ago where they mentioned it. Keyword search won't cut it—they might have said "I love blue" or "blue is my thing" or just "blue" in response to a question.

You need semantic understanding. And you need it to be fast.

Four Components, One System

MemoryStack is built around four core pieces that work together:

1. The Extraction Engine

When a conversation comes in, we don't just store the raw text. We analyze it to pull out structured information:

• Facts — "User lives in San Francisco"
• Preferences — "User prefers TypeScript over JavaScript"
• Goals — "User is building a customer support bot"
• Relationships — "User works with Sarah on the AI team"

Each extracted piece gets a confidence score. If someone says "I think I might like Python", that's different from "Python is my favorite language."

2. Vector Embeddings

Every memory gets converted into a vector—a list of numbers that captures its meaning. When you search for "programming preferences", we convert that query into a vector too, then find memories with similar vectors.

This is how we handle the "blue is my thing" problem. The vectors for "I love blue" and "blue is my favorite color" are close together in vector space, even though the words are different.

We use a hybrid approach: dense vectors for semantic similarity, sparse vectors for exact keyword matching, combined with Reciprocal Rank Fusion. This gives you the best of both worlds.

3. The Knowledge Graph

Vector search is great for finding relevant memories, but it doesn't understand relationships. That's where the knowledge graph comes in.

When you mention "John" in one conversation and "my manager" in another, the knowledge graph can connect these. It tracks entities (people, places, projects) and the relationships between them.

This enables multi-hop reasoning. "What projects is my manager working on?" requires understanding that John is your manager, then finding projects associated with John.

4. Memory Lifecycle

Human memory isn't static. Important things stay accessible; irrelevant details fade. We built the same behavior into MemoryStack:

• Reinforcement — Memories accessed frequently become stronger
• Decay — Unused memories gradually lose priority
• Consolidation — Similar memories merge to reduce redundancy
• Contradiction detection — When new info conflicts with old, we flag it

This keeps the memory system clean and relevant without manual maintenance.

Multi-Tenancy: Built for B2B

If you're building a product with MemoryStack, you need isolation between your users. Every memory in our system is scoped:

This hierarchy means you can have shared team knowledge at the organization level, app-specific context at the project level, and personal memories at the user level. Agents can be scoped to access only what they need.

Performance

Memory lookups happen in the hot path of every AI interaction. They need to be fast.

We target <100ms for search at P95, even with millions of memories. This comes from:

• Optimized vector indices with HNSW
• Aggressive caching of embeddings and frequent queries
• Horizontal scaling of the search layer
• Smart query planning that skips unnecessary work

Try It

The best way to understand MemoryStack is to use it. Our quickstart guide gets you from zero to working memory in about five minutes.