hebbmem 0.1.0

Hebbian memory for AI agents — memories that fire together wire together.

hebbmem

Hebbian memory for AI agents — memories that fire together wire together.

Install

pip install hebbmem

For higher-quality embeddings (recommended):

pip install hebbmem[ml]

Quick Start

from hebbmem import HebbMem

mem = HebbMem()

# Store memories
mem.store("Python is great for data science", importance=0.8)
mem.store("JavaScript runs in the browser", importance=0.5)
mem.store("Neural networks learn from data", importance=0.7)

# Time passes, memories decay
mem.step(5)

# Recall activates related memories through the graph
results = mem.recall("machine learning with Python", top_k=3)
for r in results:
    print(f"{r.content} (score={r.score:.3f})")

How It Works

hebbmem replaces flat vector storage with three neuroscience mechanisms:

Decay — Memories fade over time unless reinforced, following the Ebbinghaus forgetting curve. Recent and frequently accessed memories stay strong.

Hebbian Learning — Memories recalled together strengthen their connections. "Neurons that fire together wire together." Over time, the graph learns which memories are related through usage, not just embedding similarity.

Spreading Activation — Recalling one memory activates related ones through the graph, surfacing connections that keyword or vector search alone would miss.

Links

• GitHub
• Codepawl