The title made me think this was an approach that used memory editing techniques (e.g. ROME [1]) to allow an LLM's neural memory (not just its context) to change over the course of conversation. Pretty happy to realize that this is just a fancy RAG work—will be building my version of MemEditGPT soon.
Awesome, feel free to open issues or PRs to our repo if you want to contribute! It's all open source and under Apache 2.0, and we're actively looking at integrating common workflows to the CLI.
You're correct that MemGPT doesn't do editing of LLM weights like in ROME - the "memory" we're considering in MemGPT is at the text/token level, not the weight level. The core concepts behind MemGPT is giving the LLM the ability to edit a working memory scratchpad (held in-context) and reading/writing to external context via functions. An important detail is that reads are always paginated (chunked) to deal with finite context limits, and MemGPT can do many iterative read/writes from a single user input (by chaining functions together). This allows MemGPT to search over a large database of documents for example, collecting information from various sources to return an answer (as in our LlamaIndex API docs example on the README).
[1] https://arxiv.org/abs/2202.05262