Most AI agents today appear to “remember” things, but the reality is that LLMs themselves are completely stateless. The memory illusion usually comes from external systems: • conversation history stored in a DB • vector retrieval (RAG) • summarization pipelines • fact extraction services

These layers are often glued together with frameworks and APIs.

I built Mumpix to simplify that stack.

Mumpix is a lightweight memory engine designed specifically for AI agents. It runs on both the frontend and backend, and the goal is to provide a simple persistent memory layer without requiring vector databases, servers, or complex orchestration.

Install it with:

npm i mumpix

Then use it directly:

import Mumpix from "mumpix"

const db = new Mumpix()

db.set("memory^user^name", "Jane") db.set("memory^preferences^music", "jazz")

console.log(db.get("memory^user^name"))

The core ideas behind the project: • Structured agent memory using hierarchical keys • Persistent state across sessions (browser or Node) • Deterministic reads/writes instead of probabilistic vector search • Portable memory snapshots that can be exported or replayed • No infrastructure required to get started

It’s designed to behave more like SQLite for AI memory than a typical AI platform.

Some things it enables: • agents that remember user preferences locally • deterministic state tracking for agent workflows • offline AI apps with persistent memory • explainable responses (tracking which keys were read)

The core engine is intentionally small and dependency-free so it can run anywhere.

As of v1.17, Mumpix works across the full stack: • Browser (IndexedDB persistence) • Node.js • optional sync layers

I’d love feedback from people building agents or local AI systems.