nexyn-core 1.0.2

A biological memory upgrade layer for Cognee, adding Valence, Consolidation, and Decay physics to vector graphs.

Biological Memory Consolidation for Cognee

🌐 Website | 🎮 Interactive Demo

Nexyn Core is a lightweight Python library that adds human-like memory physics to your AI applications. Built specifically to run alongside Cognee, it solves a major problem in AI memory systems: context bloat.

Instead of treating every piece of information equally, Nexyn automatically scores how important a memory is (Valence), applies a decay rate, and forgets irrelevant data over time—just like a human brain.

What It Solves

• Infinite Context Clutter: AI agents quickly drown in their own logs. Nexyn ensures only the most important, reinforced memories survive long-term.
• Flat Memory Structures: Nexyn categorizes data dynamically. A passing thought decays in hours; a core user instruction (like "I am allergic to peanuts") becomes a permanent instinct.

How It Works

1. Sensory Buffer: Intercepts data before it enters your vector database.
2. Evaluator: Uses NVIDIA NIM to quickly score the emotional/logical weight (Valence).
3. Retrieval & Rehearsal: Every time you search for a memory, Nexyn resets its decay timer (rehearsal), naturally surfacing things you think about often.
4. Consolidation: A background process sweeps your database and permanently deletes memories that have decayed to zero.

📦 Installation

# Using pip
pip install nexyn-core

🚀 Usage

Nexyn is designed to be completely invisible. You simply initialize it once, and it automatically intercepts your native Cognee calls to apply biological physics. You don't need to learn a new API.

import asyncio
import cognee
import nexyn

async def main():
    # 1. Initialize Nexyn's cognitive layer
    await nexyn.inject(
        nim_api_key="nvapi-your-key-here", 
        cognee_api_key="your_cognee_api_key",
        cognee_url="https://api.cognee.ai",
        tenant_id="default",
        user_id="user_123"
    )

    # 2. Add memories normally (Nexyn automatically scores Valence)
    await cognee.add("Doug is the groom. The wedding is Sunday.")
    
    # 3. Compile the Cognee knowledge graph
    await cognee.cognify()
    
    # 4. Search triggers decay physics and memory rehearsal
    results = await cognee.search("Where is Doug?")
    
    # 5. Fast-forward time to prune dead memories
    await nexyn.sweep()

if __name__ == "__main__":
    asyncio.run(main())

🎮 Interactive Dashboard

Want to see the biological pipeline in action? Check out our live visual dashboard to watch memories decay, prune, and consolidate in real-time: 👉 Live Demo

📝 License

Distributed under the MIT License.