While doing a lot of vibe coding( not straight vibe coding, but spec driven-architecturally designed) and building full fledged application hosted on AWS and publishing to playstore, I generated an interest in agent memory and then built something around the memory, but still I wasn't convinced with the way coding agents explores the codebase by ripgrep or get-content.
So, I went deep into data science - basically graph database, gremlin, GraphSAge, A*, and many other. The thing I noticed was that " A model doesn't know what it doesn't know".
Which means, a function might have been changed or updated for 100 times, and each change carries some info, coupling, architectural descions(few times), and many such. But the agents at the time of exploring it sees the function as a static image without any layers beneath it. Sometime it might do - git diff but, a function being designed in that way carries a huge coupling with other functions over different commits. The agent barely figures out these relationships and concludes its descions merely on calls, imports, indexing of the other functions, docs, git log... But a developer trying to fix or update to accomodate a newer update, it first understands what and why of class, file, functions and related then updates.
This is why, Agents prefer on building a completely new functions, methods rather than just updating a small change in the same function.( the reason behind is that, an agent doesn't know that it doesn't know). Yes, I read github repo - ponytail which tells similar but it is mostly a smart prompting to compell the agents to grill before writing.
But, what if we provide the exact info that it misses, what if we provide, which functions are structurally similar, what if we traverse from commit -follow the edge(might be descions, update, fix, or such) land to some function states of those functions, understand them, traverse to that function, understand the clusters of those functions, read which functions are co-changed with that particular on which specific problem - then traverse the vector aided traversal with the edges leading to other functions... and so on.
I mean yes I am talking about embeddings of nodes, edges on some graph database and providing tools to the agent to explore the codebase, not only exploring, grill the codebase, understand the each bit of things before concluding any descions and recursively traverse between interconnected nodes with the help of multi-weighted edge between nodes.
I know 30% of the graph data should be populated by LLMs, but these can be done by any lightweight models or even self hosted ollama models on 16GB VRAM Mac or so.
If it sounds any good, then I have been building and had been testing for past 2 months, and results are way more efficient and effective. Token consumption literally reduced by 5x and effectiveness is still not as good as it should be because models are trained to use the cli commands, but working on that as it needs more depth knowledge of data science.
I reallly need you guys to put me on right boat if you have any things to say, grill, the comment section is all yours. - Pardon for my English
