For-Sunny/hebbian-mind-enterprise

Overview

A Python library implementing a Hebbian neural graph for AI memory, designed to simulate associative learning by strengthening connections between co-activated nodes. It provides a graph-based memory system that adapts based on usage patterns, suitable for building persistent, learning-aware applications.

Best for

Best for
Developers exploring Hebbian learning for simple adaptive memory projects

Use cases

• Building adaptive memory systems that learn from user interactions
• Implementing associative recall in recommendation or search engines
• Creating persistent context for conversational agents

How to use

Install

pip install -e .

Tools exposed

• HEBBIAN_MIND_BASE_DIR
• HEBBIAN_MIND_RAM_DISK
• HEBBIAN_MIND_RAM_DIR
• HEBBIAN_MIND_THRESHOLD
• HEBBIAN_MIND_MAX_WEIGHT
• HEBBIAN_MIND_FAISS_ENABLED
• HEBBIAN_MIND_FAISS_HOST
• HEBBIAN_MIND_FAISS_PORT
• HEBBIAN_MIND_PRECOG_ENABLED
• HEBBIAN_MIND_DECAY_ENABLED
• HEBBIAN_MIND_DECAY_BASE_RATE
• HEBBIAN_MIND_DECAY_THRESHOLD
• HEBBIAN_MIND_DECAY_IMMORTAL_THRESHOLD
• HEBBIAN_MIND_DECAY_SWEEP_INTERVAL
• HEBBIAN_MIND_EDGE_DECAY_ENABLED
• HEBBIAN_MIND_EDGE_DECAY_RATE
• HEBBIAN_MIND_EDGE_DECAY_MIN_WEIGHT

Tested with

Claude Desktop, Claude Code

Notes

A Python library implementing a Hebbian neural graph for AI memory, designed to simulate associative learning by strengthening connections between co-activated nodes. It provides a graph-based memory system that adapts based on usage patterns, suitable for building persistent, learning-aware applications.

5 stars on GitHub. Last updated 2026-03-13. Licensed MIT.

Use cases

• Building adaptive memory systems that learn from user interactions
• Implementing associative recall in recommendation or search engines
• Creating persistent context for conversational agents

Pros

• Simple Hebbian learning rule makes memory updates straightforward
• Graph structure allows flexible, relational memory representation
• Lightweight Python implementation suitable for prototyping

Cons

• No active maintenance or community support (single repo, 5 stars)
• Limited documentation and examples for production use
• Scalability and performance for large graphs not addressed

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.