Re:Show HN: Core – open source memory graph for LLMs – share...
by saxenauts on Wednesday July 02, 2025 @01:48AM
(#44440583)
I am building a graph memory too, and I agree with you. It is almost useless to generate triplets, and instead generate nodes that are usually statement strings. And can extend up to a short paragraph too.
I have strong opinions that memory should be a graph + vector hybrid. The vector store can store and index information as a cognitive fragment( ex. all things related to my house), and can keep editing it as a set of statements, while that node can be associated with other nodes (ex. my renovation plans, budgeting, etc.), because those are separate fragments. I am also using LLM to consolidate and find new patterns across the connected memories
> But one problem I see with these memory systems is that they can reduce interest on a topic once we put it in the KB.
Can you elaborate please?
My MCP has two tools - a search tool and a node adding tool. The search tool uses embedding similarity to retrieve K nodes, then expands on links and fetches another P nodes. By controlling K and P the LLM can choose to use the graph as a simple RAG or as a pure linked graph, or anywhere in-between. In practice I use Claude which is able to do deep searches. What it does not find in one call it locates in 4-5 calls.
The LLM will only add new ideas not already in the KB. It does the searching, filtering and writing. I am just directing this process. The KB can grow unbounded because when I need to add new nodes I first search the KB and find relevant nodes to link to without loading every node.
But one problem I see with these memory systems is that they can reduce interest on a topic once we put it in the KB.