Install
npm install -g github:MaxForAI/TokenlessInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Tokenless
One command to cut token usage by up to 50%+.
English · 简体中文 · 日本語 · Français · Español
Before / After · Quick start · Install · Profiles · Benchmarks & Evidence · Roadmap · Full benchmark guide
Quick start
npm install -g github:MaxForAI/Tokenless
tokenless repair-hooks --user
tokenless install-commands --user
tokenless launch
Then use Claude Code normally. Switch profiles anytime:
tokenless style chat # readable, shorter replies
tokenless style coding # dense coding output
tokenless style off # full hard-off
Claude Code gets expensive when every log, file read, diff, and long reply keeps getting carried into the next request.
Tokenless fixes that.
It keeps the raw evidence on your machine, sends Claude a compact version, and lets you expand the original output only when you need it.
Before / After
| Normal Claude Code | Claude Code with Tokenless |
|---|---|
| Reads a large file or log into future context repeatedly. | Stores the raw output locally and sends a compact packet. |
| Verbose final replies become part of the next request history. | chat and coding profiles keep replies short. |
| Agent trajectory can grow through repeated exploration and task-plan history. | Launcher trims Task/Plan tools by default; packets reduce large read context. |
Example large-read replacement:
| Raw context | Tokenless context |
|---|---|
| Full file/log output is carried through API requests. | TOKENLESS-READ-PACKET/0.1 with artifact id, imports, symbols, snippets, nearby files, and exact expansion commands. |
Why Tokenless
Claude Code sessions can become expensive because tool outputs, file reads, task-plan history, and verbose assistant replies are repeatedly carried through future API requests. Tokenless targets three sources of growth:
- Large tool output: test logs, build logs, search results, tree output, diffs, large reads, and large successful edit/write results.
- Agent trajectory overhead: repeated request context, high-overhead Task/Plan tools, and large raw file payloads.
- Response verbosity: optional
chatandcodingprofiles reduce assistant output tokens.
Benchmarks & Evidence
Tokenless has two evidence layers: real Claude Code API-body measurements, and external research showing why shorter, denser context can reduce cost without automatically reducing quality.
Real Claude Code benchmark runs
These are API-body measurements from actual Claude Code sessions. The main metric is estimated request-body or response-body tokens from raw API logs, not local hook-side savings estimates.
| Scenario | Baseline | Tokenless | Reduction |
|---|---|---|---|
5-turn CRM vibe coding, off vs coding |
4,697,867 request tokens | 2,476,391 | 47.3% |
6-turn natural conversation, off vs chat |
7,223 response tokens | 1,442 | 80.0% |
| Large CSS visual edit | 1,017,642 request tokens | 403,995-473,354 | ~54-60% |
| 10k-line React/TSX edit | 917,137 request tokens | 545,456 | 40.5% |
| Multifile React dashboard | 628,261 request tokens | 512,521 | 18.4% |
| Task/Plan tools enabled vs default launcher | 1,524,894 request tokens | 1,087,753 | 28.7% |
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