I built a graph memory MCP tool as well. I don't use triplets, instead I generate nodes. The node is composed of (id, title, text) and text can contain links inlined, like @45, referencing past nodes. So it can create both a node and its relations in one tool call.
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.
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.