TokenMizer - a local proxy for session checkpoint/resume and graph memory across Claude, GPT, and Ollama
I've been building TokenMizer, a local proxy that sits between your editor/CLI and whatever model you're using (Claude, GPT, Ollama) and handles two things I kept re-solving by hand: session checkpoint/resume, and a graph-based memory instead of a flat transcript.
The problem: once a long agent session hits the context limit, the usual fix is summarization, and summaries lose the reasoning behind a decision, not just the decision itself. I'd see a summary saying "switched to Argon2" with no trace of why bcrypt was rejected, so the agent would re-litigate the same tradeoff two sessions later. Flat transcripts have the opposite problem: everything is kept, but nothing is prioritized, so retrieval is just recency-biased keyword luck.
What TokenMizer does differently: instead of one growing text blob, decisions, constraints, and open questions are stored as nodes with edges (this decision depends on that constraint, this question was resolved by that decision). Checkpointing snapshots that graph plus a resumable session state, so you can kill a session and pick it back up without replaying the whole history through the model again.
Where it's rough: there's no eval harness yet comparing retrieval quality against a naive flat-transcript baseline, so right now my evidence is anecdotal (my own sessions), not benchmarked. I also learned the hard way that benchmarking your own memory system by asking it questions only it can answer is circular, so I'm holding off on publishing numbers until I have an honest comparison.
Repo: github.com/Shweta-Mishra-ai/tokenmizer (I'm the author). It's a Python project, MIT licensed. If you've hit the same summarization-loses-reasoning problem, I'd be interested in how you're handling it, and PRs/issues on the eval-harness gap would genuinely help.