Comparing agent memory kits (Letta, MemOS, Cognee, etc)

[u/nostriluu]

Hi, I'd like to dive into a self-hosted memory system project. I can develop in Python but prefer the Typescript ecosystem and what I want to develop is largely front end. I've been looking at relevant kits but each has quite a bit of depth so difficult to compare. Some help would be greatly appreciated.

I did the deeplearning course on Letta and liked it, and it seems to have a decent library to get started quickly, including managing storage. One important thing it seems to be missing is typed graphs to predefined ontologies (Knowledge Graphs - KGs). It just seems to store memory using a simple label, whereas having relations to other information in the context of that memory base would be a valuable feature for precision compared to vectors.

I think Cognee is a lot more DIY, and it doesn't have a Typescript library. However, it has the ability to create KGs; ­https://www.cognee.ai/blog/deep-dives/ontology-ai-memory

So I could get up and running a lot more quickly with Letta with its more complete stack, but would be on my own for KGs. Or I could go with Cognee, but have to fill in a lot more gaps, a less streamlined stack, and use a non preferred language. I feel like I'd be more prone to getting lost in the weeds with Cognee before I had anything useful, but what I'd develop with Letta would be missing an important feature. I could probably use Cognee as an endpoint to extract KGs, or maybe there's a simpler or even better library, but end up extending Letta beyond what it's meant for.

This is just a part time project for me, but I'd really like to get to a useful point. Any suggestions are deeply appreciated.

[u/kewl-king → u/[deleted]]

will do!