Tokenmizer

TokenMizer

Your AI forgets why. TokenMizer remembers.

An OpenAI-compatible proxy that builds a <b>knowledge graph</b> of your
session — decisions, files, errors, goals — and replays it when the
context window runs out. Not a summary: a queryable graph that knows
<i>"we switched from MongoDB to PostgreSQL, and here is why."</i>



One line to adopt &middot; works with Claude, GPT, Gemini, Grok, DeepSeek, Mistral, Cohere, Ollama &middot; MIT














<b>Quick start</b> &middot;
<b>See it</b> &middot;
<b>Claude Code &amp; MCP</b> &middot;
<b>Benchmarks</b> &middot;
<b>Architecture</b> &middot;
<b>Roadmap</b>

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

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The problem

Every AI session has a context limit. When you hit it, the model forgets every decision and every rationale built over hours of work, and you spend the first ten minutes of the next session re-explaining the project.

Summarising the history does not fix this. A summary tells you what was decided; it loses why, and it loses what was rejected — so the model happily re-proposes the thing you moved off three sessions ago.

Quick start

pip install "tokenmizer[anthropic,cache]"
export TOKENMIZER_ANTHROPIC_API_KEY=sk-ant-...
tokenmizer serve

Then change one line in your client:

from openai import OpenAI

client = OpenAI(
    api_key="your-key",
    base_url="http://localhost:8000/v1",   # only this changes
)

resp = client.chat.completions.create(
    model="claude-sonnet-4-6",
    messages=[{"role": "user", "content": "Continue where we left off"}],
    extra_body={"session_id": "my-project"},   # optional, enables memory
)

Everything else is unchanged: same request shape, same response shape, plus a tokenmizer block reporting what was saved. Open http://localhost:8000 and the session is already there.

Windows, Ollama, Docker, and the full step-by-step

Windows (PowerShell)

$env:TOKENMIZER_ANTHROPIC_API_KEY = "sk-ant-..."   # this session
setx TOKENMIZER_ANTHROPIC_API_KEY "sk-ant-..."     # persistent

No API key? Ollama runs locally and free:

ollama pull llama3
pip install tokenmizer
# then set `provider: ollama` in tokenmizer.yaml

Docker

docker compose up -d

Full installation notes, every provider's environment variable, and the configuration reference are in docs/configuration.md and docs/deployment.md.

See it

Nothing here is a mockup. Every screenshot and the demo below are the shipped UI rendering a session from the labelled corpus in benchmarks/eval/corpus.

The session graph is a page you drive, not a picture

Radial → select a node → force → filter a type → timeline → light. Regenerate with python scripts/gen_graph_demo.py.