AI agent memory using Modern Hopfield Networks — no LLM calls, no database, one matrix multiply. MCP server for Cursor, Claude Code, and other AI coding agents.

MHN AI Agent Memory

Associative Memory for AI Agents Using Modern Hopfield Networks

A Python library for deterministic associative memory in AI agents.

Install • Quick Start • Features • How It Works • API Reference

          ┌─────────────────────────────────────────────┐
          │                                             │
          │   xi_new = X @ softmax( beta * X^T @ xi )  │
          │                                             │
          │   One equation. One matrix multiply.        │
          │   Deterministic. Microseconds. Free.        │
          │                                             │
          └─────────────────────────────────────────────┘

What is this?

Many agent memory systems store text in a database, then ask an LLM to interpret it at query time. This library does something else: it applies the Modern Hopfield Network update rule directly, exposing transformer-style attention as an explicit, controllable memory.

1. Store text in a database
2. Call an LLM to reason about it (costs money, takes seconds, non-deterministic)
3. Hope the LLM doesn't hallucinate about what it stored