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.
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.
Any advice would be greatly appreciated right now all we have is the player that lets you control the gifs with your finger and a few other options what do you guys think