I'm not gonna lie - at the moment it's pretty basic as a chunked semantic store where relevant chunks are retrieved in conjunction with some criteria the Agent can pass to the MCP server.
Context usage is definitely a problem that's on my mind also and semantic anchors are one area I'm exploring but don't have a clear architecture for it jotted down yet. The real problem I'm facing right now is how to inject this into say claude or chatgpt and have those agents default use it as a memory layer
That is what I do at the moment - I gotta update the instructions on the website to prod users to set things up this way.
The issue I perceive though is that adding an MCP server alone is not enough to modify the system prompt of the AI Agent. I tried to have the mcp server description be an injected prompt to add these instructions to the system prompt but that doesn't seem to work, I tried adding sampling to the MCP server which supposedly should be able to plug into messages without luck, tried to optimise for chatGPT with an OpenAPI spec etc.
The only way I found that I can get those clients to use my memory layer is by doing what you describe - which is not necessarily the most user friendly/one-click setup I desire
At the moment I'm doing a hybrid but that's still something that needs a lot of trial and error to get right. Doing turn-based context with auto compacting at the moment, but I'm sure that'll change a few more times before it works well
Context usage is definitely a problem that's on my mind also and semantic anchors are one area I'm exploring but don't have a clear architecture for it jotted down yet. The real problem I'm facing right now is how to inject this into say claude or chatgpt and have those agents default use it as a memory layer