[Integration Proposal] LLM RAG Booster — Gravitational Compression as a Storage Backend for MemOS

[Tryboy869]

Hi MemOS team 👋

I'm Daouda Abdoul Anzize, builder of LLM RAG Booster — a zero-dependency memory compression library for LLMs that uses quantum-inspired gravitational encoding to compress document chunks at 8.1× ratio with 100% integrity guarantee.

I'd like to propose an integration between LLM RAG Booster and MemOS as a lightweight, compressed storage backend — specifically for environments where vector databases are not available or desirable (edge devices, mobile, low-resource settings).

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What LLM RAG Booster brings

Feature	Value
Compression	8.1× validated on CPython codebase
Integrity	100% (hash-verified per chunk)
Dependencies	Zero (pure Python + requests)
Setup	Single file, 3 lines of code
LLM agnostic	Works with any API (OpenAI, Groq, Anthropic, Ollama)

How it works :

Inspired by the orbital structure of hydrogen atoms, each document chunk is encoded into a GravitationalBit — a structure with up to 5,525 orbital states (n_max=25). The chunk's SHA-256 hash is stored across these states. On retrieval, keyword scoring identifies the top-K relevant chunks, and only their original text is passed to the LLM as context.

from booster import LLMRAGBooster

booster = LLMRAGBooster(api_url="...", api_key="...", model="llama-3.3-70b-versatile")
booster.load_document(large_text)  # 8.1× compressed, 100% integrity
answer = booster.ask("What is the main conclusion?")

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Proposed integration

MemOS currently uses SQLite + hybrid FTS5/vector retrieval for persistent memory. LLM RAG Booster could complement this as:

1. A lightweight storage tier — for environments without Neo4j, Qdrant, or Redis
2. A compression layer — reducing storage footprint for long-term memory cubes
3. An offline/edge mode — zero cloud dependency, 100% local

A possible integration point would be a MemCube backend adapter:

# Concept
from memos import MemOS
from booster import GravitationalMemory

mem = MemOS(storage_backend=GravitationalMemory(n_max=15))
mem.add("User prefers concise answers")
mem.search("How does this user like to communicate?")

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Links

• GitHub : https://github.com/Tryboy869/llm-rag-booster-allpath
• PyPI : pip install requests (single file, no package yet — download booster.py directly)
• Validated on : CPython codebase (50k+ lines), Llama-3.3-70B via Groq

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Question for the team

Would you be open to exploring this integration? I'm happy to implement a GravitationalMemCube adapter as a PR or as a plugin, depending on what fits MemOS architecture best.

Happy to discuss on Discord too.

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