tokenmizer

TokenMizer

Keep your AI context alive across sessions.

Graph-backed memory · session checkpointing · intelligent compression
Drop-in proxy for Claude, GPT, Gemini, Grok, DeepSeek, Ollama — any LLM.

Quick Start · How it works · Benchmarks · Claude Code · Contributing

_{Real run: 25-node graph, checkpoint ckpt_21a0959c3ddf, 233-token resume. Regenerate with python scripts/gen_demo_gif.py.}

The Problem

Every AI session has a context limit. When you hit it:

• The model forgets every decision, rationale, and context built over hours
• You waste 10–30 minutes re-explaining the project every new session
• Large files (CSV, PDF, Excel) eat your entire token budget instantly

How TokenMizer Solves It

TokenMizer is a local proxy between your app and any LLM. Every request goes through a pipeline that builds a live knowledge graph, compresses inputs, caches responses, and auto-checkpoints before context runs out.

Your App  →  TokenMizer (:8000)  →  Claude / GPT / Gemini / any LLM
                    │
          ┌─────────┴──────────────┐
          │   6-Layer Pipeline     │
          │   L0  File Intel       │  CSV/PDF/Excel → schema + sample
          │   L1  Compression      │  15–40% input reduction
          │   L2  Output Trim      │  5–15% output reduction
          │   L3  Semantic Cache   │  100% on repeated queries
          │   L4  Graph Memory     │  session continuity
          │   L5  Prompt Cache     │  90% on repeated system prompts
          └────────────────────────┘

Architecture

Decision Memory — 4-State Model

History is never deleted. "Why did we switch from React to Next.js?" — always answerable.

Quick Start

python --version

pip install "tokenmizer[anthropic,cache]"

setx TOKENMIZER_ANTHROPIC_API_KEY "sk-ant-YOUR-KEY"

export TOKENMIZER_ANTHROPIC_API_KEY=sk-ant-YOUR-KEY

tokenmizer serve