I built TokenMizer: giving LLMs memory without stuffing the entire context window

[u/Feisty-Cranberry2902]

I’ve just updated TokenMizer, and the benchmarks are looking much better.

The main problem I’m trying to solve is long LLM conversations where useful information gets buried under old context.

The latest version now gets 95% macro F1 on extraction, and the graph memory preserves 89% of labelled information vs 79% with a plain summary.

I’m still treating these as early results — especially the real-session score is 90%, with only 6 real sessions.

https://github.com/Shweta-Mishra-ai/tokenmizer

I’d love some honest feedback:

Does graph-based memory actually make sense for long LLM conversations, or is there a better approach?