[Integration Proposal] LLM RAG Booster — Gravitational Compression to reduce SimpleMem token footprint

[Tryboy869]

Hi SimpleMem team 👋

I'm Daouda, builder of LLM RAG Booster — a zero-dependency compression layer for LLM memory that achieves 8.1× compression with 100% integrity guarantee, using quantum-inspired gravitational encoding.

SimpleMem's core strength is semantic lossless compression of dialogue into atomic memory units. I'd like to propose an integration where LLM RAG Booster acts as a storage compression layer on top of SimpleMem's atomic entries — reducing the token footprint further when retrieving context for the LLM.

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The complementarity

SimpleMem and LLM RAG Booster solve adjacent problems:

	SimpleMem	LLM RAG Booster
Focus	Semantic compression of dialogues	Storage compression of chunks
Method	LLM-driven atomic encoding	Gravitational Bit encoding
Output	Atomic, timestamped memory units	Compressed chunks, keyword-indexed
Retrieval	Intent-aware 3-stage pipeline	Top-K keyword scoring
Dependencies	LLM API	Zero (pure Python)

The combination :

SimpleMem produces high-quality atomic memory units → LLM RAG Booster compresses them for storage → on retrieval, only relevant units are decompressed and passed to the LLM.

This could reduce the already-efficient SimpleMem token usage even further, especially for long-running agents with hundreds of memory units.

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Concrete proposal

# Concept — SimpleMem producing atoms, Booster storing them
from simplemem import SimpleMem
from booster import GravitationalMemory

mem   = SimpleMem()
store = GravitationalMemory(n_max=15)  # 1,240 states per atom

# Stage 1 — SimpleMem compresses dialogue into atomic units
mem.add_dialogue("Alice", "My name is Daouda, I work on AI memory.", "2026-01-01T10:00:00")
atoms = mem.finalize()  # → atomic, timestamped facts

# Stage 2 — Booster compresses and stores atoms
for atom in atoms:
    store.store(atom.text, atom.timestamp)

# Stage 3 — Intent-aware retrieval + Booster decompression
answer = mem.ask("What is my name?", storage=store)

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Links

• GitHub : https://github.com/Tryboy869/llm-rag-booster-allpath
• Validated on : CPython (50k+ lines), Llama-3.3-70B (Groq), 5/5 questions answered
• Compression : 8.1× on 2728-word document

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Question

Would you be open to a discussion about using LLM RAG Booster as an optional storage backend in SimpleMem? I can prototype an adapter and open a draft PR.

Also curious: does SimpleMem currently have a pluggable storage interface, or is it tightly coupled to the current DB layer?

— Daouda Abdoul Anzize
📧 anzize.contact@proton.me
🐦 @Nexusstudio100