🦀 Kōhaku

🧠 Episodic memory engine for LLMs — persistent, associative, and beyond context windows.

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🍂 Meaning

Kōhaku (琥珀) — amber, preserved in time.

Like insects trapped in amber, memories are captured, compressed, and preserved — not lost to context limits.

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🚀 What it is

Kōhaku is a neural episodic memory system:

• Stores experiences as HDC hypervectors
• Retrieves via associative similarity
• Works as a drop-in memory layer for any LLM

Not:

• ❌ RAG
• ❌ vector database

But:

✅ learned memory with recall

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❗ The problem

LLMs forget.

• Context windows are finite
• RAG loses nuance
• Summaries lose detail

There is no true memory system.

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🧠 What you learn

• Hyperdimensional computing (HDC)
• Associative memory / Hopfield networks
• Memory-augmented architectures
• Episodic vs semantic memory

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⚙️ Architecture

• 🦀 Rust core — high-performance HDC engine
• 🐍 Python API — LLM integration

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🚀 Quick Start

pip install kohaku

from kohaku import Memory

mem = Memory()
mem.store("User prefers Italian wine")
mem.query("What does the user like?")

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🎯 Vision

Give models memory — not just context.