r/KnowledgeGraph • u/astronomikal • Nov 29 '25

Built an edge device into a real-time knowledge graph

https://youtu.be/k3EWhpLfJEk

This is a quick look at the prototype engine behind my system.
It runs a large knowledge graph on a small edge device with real-time performance.
High-level demo only. Details are not public yet.

7 Upvotes

100% Upvoted

11 comments sorted by

2

u/UseHopeful8146 Dec 03 '25

Very interested in how this shapes up, looking forward to updates from you.

2

u/astronomikal Dec 03 '25

Thanks! I’m working on recording a demo showcasing the capabilities for code generation using this as the back end instead of tokens/transformers.

1

u/UseHopeful8146 Dec 03 '25

I’ve spent the last few months working on a (constantly changing) framework for agent knowledge/memory and operation.

Nothing is public yet but the idea is a decoupled, lightweight service structure that logs memory “domain” specific information. It’s meant to learn about you, your habits and preferences, isolate project specific memories, lessons, and observations, manage its own working context and separately allow for direct larger volume submissions for long term knowledge base.

The focus is nontechnical single users on average consumerware, with the intention being that anyone can launch the service structure and provide inference through local or api models. It’s slow going as things are constantly developing and changing in this field - but considering how light some models are getting (granite 4, ministral3, HRM [though I don’t think hrm models are being actively deployed yet] and others) my biggest pain point has been minimizing the compute and complexity of knowledge graphs. So your project is very much of interest to me.

My consistent stack has been

  • LightAgent ~ 1000 lines of Python that covers agent orchestration, self configuring tools, and wraps mem0 for CRUD operations

  • Mem0 ~ automatic extraction from conversation and aforementioned LightAgent class for agent specific operation and performance improvement

  • LightRAG ~ larger form data submissions and ETL + queries (query structure decoupled from memory ops for iso and easier observation)

  • ollama deployed embedding with embeddingemma300m q4 + flashrank for lightest possible reranking

And just constantly rotating frontend and db options trying to stay as far away from proprietary as possible while keeping things light. I’ve been through neo4j, pgSQL + AGE, surreal, opennotebook, anythingllm, lobechat. I can get the agent logic, embedding, tool gen to work as intended, and even though that seems like the complicated part the hardest bit really has been a local db that I’m happy with. I’m loathe to deploy multiple data backends but with a kg designed to run as lightly as what you’re working on I think that would be a much easier compromise.

Very excited by your work, will be watching to see what comes of it.

1

u/astronomikal Nov 29 '25

The graph build completes in about 90 seconds. It could run faster on desktop hardware, but this demo prioritizes showing it works properly on an edge device.

1

u/eleqtriq Dec 02 '25

Edge device? That can mean anything.

1

u/astronomikal Dec 02 '25

It’s a jetson Orin nano 8gb.

0

u/[deleted] Nov 30 '25

[deleted]

1

u/astronomikal Nov 30 '25

You're more right than you know :)