token-diet 🍽️

Save tokens while coding with AI. Use less, get the same (or better) results.

When you use AI coding tools like Claude Code, Codex, or Cursor — they send your entire codebase and chat history to the AI every single time. That's expensive and actually makes the AI dumber (too much noise buries the important stuff).

token-diet fixes that. It sends a smart summary of your code instead of every file, remembers decisions instead of replaying the whole conversation, reuses cached prompts so repeated parts are nearly free, and makes the AI reply with just the changed lines instead of rewriting entire files.

The result: you spend less on tokens, and the AI gives better answers because it can focus on what matters.

Results

Architecture

flowchart TB
    subgraph Client["Your AI tool"]
        CC["Claude Code / Cowork / Codex / Cursor"]
    end

    subgraph Frontends["How you connect"]
        MCP["Slash commands (/map, /focus, etc.)<br/>Works in Claude Code, Cowork"]
        PROXY["Local proxy (localhost:8000)<br/>Works with any AI tool"]
    end

    subgraph Engine["What happens behind the scenes"]
        ASM["Prompt builder<br/>Stacks info in the smartest order"]
        BUD["Token counter<br/>Measures exact cost, cuts the fat"]
        IDX["Code scanner<br/>Reads your files, extracts structure"]
        MAP["Smart code map<br/>Ranks what's important right now"]
        MEM["Memory<br/>Remembers decisions, forgets chit-chat"]
        DOC["Doc converter<br/>Turns PDFs/Word docs into clean text"]
        PATCH["Edit applier<br/>Takes AI's changes, patches your files"]
        ROUTE["Model picker<br/>Uses cheap AI for simple tasks"]
        TEL["Cost tracker<br/>Shows how much you saved"]
    end

    subgraph Store["Saved data (per project)"]
        DB[("Code index<br/>SQLite database")]
        LOG[("Decision log<br/>What was decided & why")]
        CACHE[("Cache<br/>Avoids re-doing work")]
    end

    PROV["AI provider<br/>Anthropic / OpenAI"]

    CC -->|"your message"| MCP
    CC -->|"your message"| PROXY
    MCP --> ASM
    PROXY --> ASM
    ASM --> BUD
    ASM --> MAP
    ASM --> MEM
    ASM --> DOC
    MAP --> IDX
    IDX --- DB
    MEM --- LOG
    DOC --- CACHE
    ASM -->|"optimized prompt"| ROUTE
    ROUTE --> PROV
    PROV -->|"just the changes"| PATCH
    PATCH -->|"edits applied"| CC
    ROUTE --> TEL
    TEL --- CACHE

How it flows: Your message → token-diet builds a lean prompt (code map + decisions + just what's needed) → picks the right AI model → AI replies with only the changed lines → edits applied to your files. Every request is tracked so you can see your savings.

Install

Quickest way (from GitHub):

pip install 'git+https://github.com/aryxnsdfs/token-diet'

With all features:

pip install 'token-diet[all]'

For development:

git clone https://github.com/aryxnsdfs/token-diet
cd token-diet
pip install -e '.[all,dev]'

Quick start

cd your-project          # go to any project you're working on
ctx init                 # one-time setup: scans your code, creates config
ctx doctor               # check everything is working

Then open Claude Code in that project and press / — you'll see the commands. Type /begin as your first message to begin a session.

Commands

Works with

See docs/INSTALL.md for detailed setup per tool.

How it saves tokens

Project structure

ctx/
  cli.py          Command line: init, serve, doctor, proxy
  registry.py     All commands defined in one place
  server.py       Connects to Claude Code (MCP protocol)
  proxy.py        Connects to any tool (local web server)
  init/           Setup scripts for each AI tool
  engine/         The brain: code scanner, map builder, memory, etc.
tests/            Automated tests to make sure nothing breaks

License

MIT — use it however you want.