TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management

arXiv:2606.06337v2 Announce Type: replace Abstract: Long-horizon LLM sessions outlive their context windows, and the standard mitigations - truncation, summarization, retrieval - share a structural flaw: they treat history as flat text, discarding precisely the content that makes a session resumable: decisions and their rationales, task status, and file modification history. We present TokenMizer, an open-source transparent proxy that maintains session history as a typed knowledge graph and, at context boundaries, replaces the raw transcript with a token-budgeted serialization of session state. The schema comprises 14 node types and 7 edge types under an 8-state lifecycle in which decisions can be superseded or explicitly invalidated; bitemporal validity intervals support time-travel queries; and first-class decision-transition records preserve why each decision replaced its predecessor (trigger, reason, evidence). Version 0.3.1 embeds this memory core in a production-shaped serving layer - SSE streaming, security middleware, nine provider adapters, a monitoring dashboard, graph exports (D3 JSON, self-contained interactive HTML, Obsidian Canvas) - and exposes checkpoint/resume to agents as Model Context Protocol tools. The evaluation is deliberately minimal and fully provenanced: three synthetic sessions, heuristic-only extraction, one plain-summary baseline, every value traceable to a single versioned results file. Graph extraction ties the baseline on task recall (75.6%) and exceeds it on decision recall (85.0% vs. 70.0%) and file recall (100% vs. 91.7%), with 201-302-token resume blocks extracted in 8.1-529.9 ms per session. At n=3 these results are directional; ceiling effects and baseline weaknesses are analyzed explicitly. Code, benchmark runner, and the exact results file are released under the MIT licence.