With the latest GPT-5 I think it has done a great job at solving the needle in a haystack problem and finding the relevant files to change to build out my feature/solve my bug. Although, I still feel that it lacks some basic context around the codebase that really improves the quality of the response. Currently, the way agentic development works is that we do a semantic search using RAG (dense search) over our codebase and find the most relevant code or grep (sparse search) to solve our given problem/feature request.

I think that's great. But I also think that it gives room for improvement on how we think of context. Most time documentation is hidden in some architectural design review in a tool like notion, confluence, etc. Those are great for human retrieval but even then it is often time forgotten when we implement the code functionality. Another key issue is that as the code evolves, our documentation becomes stale.

We need a tool that follows the agentic approach we are starting to see where we have ever-evolving documentation, or memories, that our agents could utilize without another needle in a haystack problem.

For the past few weeks I have been building an open source MCP server that allows for the creation of "notes" that are specifically anchored to files that AI agents could retrieve, create, summarize, search, and ultimately clean up.

This has solved a lot of issues for me.

You get the correct context of why AI Agents did certain things, and gotchas that might have occurred not usually documented or commented on a regular basis.

It just works out-of-the-box without a crazy amount of lift initially.

It improves as your code evolves.

It is completely local as part of your github repository. No complicated vector databases. Just file anchors on files.

I would love to hear your thoughts if I am approaching the problem completely wrong, or have advice on how to improve the system.

You can find the project at https://github.com/a24z-ai/a24z-Memory.