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

TokenMizer is an open-source proxy system that addresses a critical constraint in LLM deployments: managing long-horizon tasks within finite context windows. By modeling session history as a typed knowledge graph rather than flat text, TokenMizer achieves 50% smaller resume blocks while preserving architectural decisions and task rationale that traditional baselines lose.

TokenMizer tackles a fundamental operational challenge facing LLM systems in production environments. As language models handle increasingly complex, multi-session tasks—from software engineering to research—their fixed context windows become a bottleneck that forces critical information loss. Traditional approaches compress session history into flat text summaries, destroying the relational structure needed to resume work meaningfully. This technical limitation has direct business implications: systems that lose architectural context or task rationale require expensive manual re-briefing, slowing workflows and increasing computational overhead through redundant queries.

The solution structures session memory as a typed knowledge graph with 14 node types and 7 edge types, fundamentally preserving how information relates rather than merely what information exists. The three-tier checkpoint system combined with an 8-layer compression pipeline demonstrates serious engineering maturity. Field testing across 21 sessions spanning five domains shows TokenMizer produces resume blocks averaging 78 tokens versus 159-170 tokens for baselines—while improving decision recall by 9-17 percentage points.

This advancement matters significantly for developers building production LLM systems. Current deployed solutions waste tokens on context management, increasing operational costs and latency. Organizations running long-horizon AI workflows—particularly in software development, research, and complex analysis—face tangible efficiency gains. The semantic cache component further reduces latency on repeated queries, creating compounding improvements.

Future development should focus on standardizing graph schemas across diverse domains and integrating TokenMizer with major LLM platforms. Open-source deployment removes vendor lock-in concerns while enabling community-driven schema improvements. The domain variance in results (software engineering outperforming research reasoning) suggests tailored schemas could unlock additional efficiency gains.

• →TokenMizer reduces session resume blocks to 2x smaller size (78 vs 159-170 tokens) while preserving critical architectural context that flat-text baselines discard.
• →Knowledge graph structure achieves 46.6-58.7% task and decision recall by capturing relational information rather than treating history as unstructured text.
• →Heuristic compression delivers 47.3% token reduction with zero external dependencies, directly lowering operational costs for production LLM systems.
• →Domain heterogeneity in results indicates explicit imperative tasks (software engineering) benefit most, while implicit reasoning tasks show lower recall rates.
• →Open-source release enables adoption in production workflows without vendor dependency, creating immediate efficiency gains for long-horizon LLM deployments.