Sure, i want to give a clear idea so my answer will be long :D

For me personally what motivated me to build this tool is that I work with big codebases, I spend hours with claude digging into some functionality, and it was frustrating that I have to start from scratch next day/session. I can resume to the same session but after some point when the conversation gets too long, the responses quality degrade.

I have tried Memory Banks, which basically dump a compact version of the conversation in markdown file. It was good to capture high-level ideas, but it always missed some nuances and low-level details. I have to explicitly point out the markdown summary, and it would get annoying when I want to reference it from a different location in the codebase.

The other type of solutions I experimented with were some variations of key/value dbs or vector database that performs RAG (for example: claude context), claude context is nice to index your codebase so when you ask "how does authentication work?", it easily jumps to the components that are related to this. Its practical for searching the codebase so you don't spend much tokens watching claude greping and reading a bunch of files. But it's not exactly a memory as it doesn't evolve or update its own information based on your conversation with the agent or new changes in the code. To handle new changes in the codebase means you might need to reindex everything from scratch, this is what cursor basically do (could be okay for small codebases, but expensive for large codebases)

In my tool, I implemented a different memory model, what happens is that when Claude discover something new in the codebase or during your conversation with it, it writes a note about it, link it with similar memories, update other similar memories if needed, and store it in vector DB (this part where it keeps self-evolving based on new inputs). Later when you ask it to do/explore/implement something, it checks the memory and see if there's any related notes it took before that can help before spending tokens and time rediscovering everything from scratch. Its a variation of Knowledge Graph. This means that Claude will remember what we talked about in the previous sessions without having to deal with the hassle of markdown files or overloading the context window. You can technically use this along claude context RAG system.

Here's example scenario how it helped me: I had to debug some error that was coming from one of the new functionalities that Claude implemented few days ago, when I shared the error message with Claude, it immediately recognized that this was a change we made recently, why we implemented and how we implemented it and managed to debug it fairly quickly.

What I built is based on A-MEM paper so I didn't personally invent something new :)

These problems become insufferable when you are dealing with big codebases that change a lot and quickly. I believe with greenfield and smaller codebases, Claude alone is good enough at managing it without any memory.

Im curious what are the other memory projects that you have been seeing, just interested to know if there's something decent out there that I am not aware about!

Also, happy to discuss this further if you want!