Stop Throwing Money at AI: 10 Open‑Source Tools Cut Claude Code Tokens by 80% and Slash Large Projects by 49×

Output compression tool

Caveman removes all boilerplate and filler from AI replies. Internal tests show an average token reduction of 65 % and a maximum of 87 % while preserving 100 % technical accuracy and delivering responses three times faster.

Typical Claude reply to a React re‑render issue: 69 tokens (includes filler such as "I can help you" and "this is a common problem").

Caveman mode: 19 tokens, directly stating the cause and fix (e.g., useMemo).

Benchmark examples:

Explain React re‑render bug – 1180 → 159 tokens (87 % saved).

Fix Auth middleware token expiry – 704 → 121 tokens (83 % saved).

Debug PostgreSQL race condition – 1200 → 232 tokens (81 % saved).

Implement React error boundary – 3454 → 456 tokens (87 % saved).

Installation (macOS/Linux/WSL):

# macOS/Linux/WSL
curl -fsSL https://raw.githubusercontent.com/JuliusBrussee/caveman/main/install.sh | bash

Repository: http://github.com/juliusbrussee/caveman

Command‑output filtering tool

RTK (Rust Token Killer) is a high‑performance, dependency‑free CLI proxy written in Rust. Startup overhead is under 10 ms and it automatically discards noisy command‑line output such as progress bars and repeated logs.

In a 30‑minute Claude Code session total tokens dropped from 118 000 to 23 900, an 80 % reduction.

ls/tree: 2000 → 400 tokens (80 % saved).

cat/read: 40 000 → 12 000 tokens (70 % saved).

grep/rg: 16 000 → 3200 tokens (80 % saved).

git status: 3000 → 600 tokens (80 % saved).

git diff: 10 000 → 2500 tokens (75 % saved).

cargo/npm test: 25 000 → 2500 tokens (90 % saved).

Installation (macOS/Linux):

brew install rtk
# or generic Linux/macOS
curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh

Repository: http://github.com/rtk-ai/rtk

Code‑base context optimization tool

Code Review Graph parses an entire repository with Tree‑sitter into a graph of functions, classes, imports and their relationships. For each task it sends only the code directly relevant to the request, avoiding the 99 % of unrelated files.

Tests on a Next.js monorepo (27 732 files) reduced the number of files read from the whole repository to 15, saving 49 × tokens. Average token reduction across projects was 8.2 ×.

Gin: 21 972 → 1 153 tokens (≈16.4× reduction).

Flask: 44 751 → 4 252 tokens (≈9.1× reduction).

Next.js: 9 882 → 1 249 tokens (≈8.0× reduction).

FastAPI: 4 944 → 614 tokens (≈8.1× reduction).

Key features:

Explosion‑radius analysis – automatically tracks all callers, dependencies and tests affected by a changed function.

Incremental updates – re‑parses only changed files; a 2 900‑file project updates in under 2 seconds.

Supports 24 languages plus Jupyter notebooks (Python, TypeScript, Go, Rust, Zig, etc.).

Installation:

pip install code-review-graph
code-review-graph install
code-review-graph build

Repository: http://github.com/tirth8205/code-review-graph

Other specialized tools

Context Mode stores raw AI output in a local SQLite database, cutting log‑related context by 98 % (repo: http://github.com/mksglu/context-mode).

Claude Token Optimizer compresses project‑level prompt templates from 11 k to 1.3 k tokens (≈90 % saved) (repo: http://github.com/nadimtuhin/claude-token-optimizer).

Token Optimizer removes invisible characters and redundant markers, reclaiming 10‑30 % of context space (repo: http://github.com/alexgreensh/token-optimizer).

Token Optimizer MCP adds aggressive caching and compression to MCP tools, saving >95 % of MCP‑related tokens (repo: http://github.com/ooples/token-optimizer-mcp).

Claude Context (Zilliz) turns the whole repo into a searchable vector store, reducing cost by ~40 % (repo: http://github.com/zilliztech/claude-context).

Claude Token Efficient uses a single CLAUDE.md file to force concise AI output without code changes (repo: http://github.com/drona23/claude-token-efficient).

Token Savior navigates by symbols (functions, classes) instead of whole files, reducing navigation tokens by 97 % and providing persistent memory (repo: http://github.com/mibayy/token-savior).

Tool‑selection guide

Huge monorepos: combine Code Review Graph + Token Savior .

Frequent terminal commands (tests, builds, Git): use RTK .

Heavy MCP usage or massive logs/GitHub data: adopt Context Mode .

Quick win without workflow changes: pair Caveman with Claude Token Efficient .

Ten habits that save tokens without any tool

Edit the original prompt instead of adding follow‑up questions; each new message forces the model to reread the entire history.

Start a new session every 15‑20 messages; most token waste comes from rereading old conversation history.

Batch multiple questions into a single prompt; merging three separate queries saves more than half the tokens.

Upload reusable files to Claude’s Projects feature once, then reference them instead of re‑uploading each session.

Store static context (e.g., "I am a front‑end developer using React and need concise commented code") in Claude’s Memory to avoid repeating it.

Disable unused features such as web search, connectors, or advanced reasoning, which each add extra tokens per response.

Use low‑cost models (e.g., Claude Haiku) for simple tasks like syntax checks, formatting, brainstorming, or translation; Haiku costs 75 % less than Sonnet and 90 % less than Opus.

Spread usage across the day; Claude’s quota rolls over every five hours, so avoid burning it all in one morning.

Run heavy tasks (large refactors, full‑repo scans) outside peak US hours to avoid higher consumption rates.

Enable “over‑usage as safety net” for Pro/Max plans, setting a monthly cap to prevent sudden quota exhaustion while staying within budget.

Community project: Local LLM Proxy

Local LLM Proxy lets users pool saved token credits across time zones, models and regions, creating a collaborative community cache.

Repository: https://github.com/wink-run/local-llm-proxy