Stop Wasting Your Tokens! 6 Repos for LLM Token Optimization

TL;DR

• LLM token optimization is mandatory for keeping AI agent costs under control and context windows clean.
• Output compressors like RTK and Context Mode prevent bloated terminal logs from burning your budget.
• Code navigation tools like Code Review Graph and Token Savior stop LLMs from reading massive, irrelevant files.
• Prompt adjusters like Caveman Claude and Claude Token Efficient force AI models to generate hyper-concise responses.

The Reality of Context Bloat

In 2026, building software with AI agents means dealing with massive token burns. You run a simple test command, and suddenly your agent dumps 50,000 characters of JSON output into the conversation. Because API bills scale linearly with your context size, that tiny mistake gets incredibly expensive fast. That is exactly where LLM token optimization comes into play. Once you optimize what goes into the model, your operational costs drop dramatically.

How does token waste happen?

Tokens vanish when AI agents decide to read entire files just to fix a single line of code. They also disappear when command line tools return verbose logs filled with ansi color codes and repetitive errors. The moment you hand an unoptimized terminal stream to a large language model, you are actively burning money. So, let us look at six brilliant repositories that solve this exact problem and make your workflows incredibly lean.

1. RTK - Rust Token Killer

RTK - Rust Token Killer is a blazing fast CLI proxy built to intercept noisy terminal outputs. It acts as a smart filter between your developer tools and your AI agent's context window.

Key Features

When you run commands like git diff or ls, RTK compresses the output by up to 90 percent. Which means Claude only sees the essential structural data. It strips out whitespace, groups similar logs together, and truncates redundant error traces. Because it is distributed as a single Rust binary, the execution overhead is strictly under 10ms.

Setup and Installation

You can easily wrap your everyday commands with RTK to see instant savings.

cargo install rtk
rtk git status
rtk grep "pattern" .

2. Context Mode

Context Mode tackles the context problem by sandboxing tool outputs entirely. Since typical tools dump raw data straight into the prompt, Context Mode intercepts them before they can cause damage.

Key Features

It tracks every file edit and git operation in a local SQLite database. When the conversation compacts, it uses BM25 search to retrieve only what is genuinely relevant. And then, it allows the agent to run analysis scripts inside an isolated sandbox, returning just the final computed results. That's why users consistently report up to a 98 percent reduction in context bloat.

Setup and Installation

Context mode runs seamlessly as a background server.

npm install -g context-mode

3. Code Review Graph

If your agent constantly re-reads thousands of files to understand a one-line change, Code Review Graph is the perfect solution. It builds a persistent, structural map of your entire codebase.

Key Features

Using Tree-sitter, it parses your project into an AST and stores it locally. The whole time you are coding, it incrementally updates this map in the background. Once you ask for a review, the LLM queries the graph via the Model Context Protocol (MCP) to understand the blast radius of your changes. It completely replaces reading entire source files with a compact structural context.

Setup and Installation

Installation takes seconds and configures your platforms automatically.

pip install code-review-graph
code-review-graph install --platform claude-code

4. Token Savior

Token Savior is an advanced MCP server that revolutionizes codebase navigation. It actively stops the LLM from executing naive reads on massive project files.

Key Features

Instead of blind file reads, Token Savior exposes targeted symbol-level queries. The model navigates your codebase by requesting specific functions or classes. But the real magic is its persistent memory engine. Every decision and bugfix gets stored in a local database, which is intelligently injected into future sessions. This results in incredibly efficient continuous interactions without the overhead of repeating context.

Setup and Installation

You can load it directly into your workspace.

uvx token-savior

5. Caveman Claude

Sometimes the easiest way to save money is to tell your AI to stop being so polite. Caveman Claude forces your agent to speak in highly compressed, stripped down English.

Key Features

The plugin aggressively removes filler words, apologies, and unnecessary pleasantries. It maintains absolute technical accuracy but cuts output token usage by about 75 percent. For heavy debugging sessions, shorter responses mean significantly lower latency. You get your answers faster, and your monthly bill shrinks.

Setup and Installation

Adding it to your terminal agent is a one-line process.

claude plugin marketplace add JuliusBrussee/caveman
claude plugin install caveman@caveman

6. Claude Token Efficient

If you want the absolute easiest setup with zero dependencies, Claude Token Efficient is the repository you need. It relies entirely on a single drop-in file.

Key Features

You simply place a CLAUDE.md file directly in your project root. It provides strict rules that force terse responses, skipping preambles and prioritizing raw code over lengthy explanations. Because user instructions override default agent behavior, this passive method routinely cuts output verbosity by 63 percent. It is the perfect passive approach to continuous LLM token optimization.

Setup and Installation

No commands are required. Just grab the file from the repository and drop it into your workspace folder.

Implementation Guide

Let us see how these tools stack up against each other so you can pick the right one for your stack.

What are the best practices for context management?

You should always combine an input compressor like RTK with an output reducer like Caveman Claude. Since they operate on different sides of the data pipeline, their overall savings compound beautifully. The goal is to establish strict boundaries around what your models are allowed to read and write.

Is prompt caching enough?

Caching helps tremendously, but it only reduces the cost of repeated prefixes. You still pay for the execution phase. Reducing the actual payload sizes ensures you stay far away from context window limits in the first place.

Conclusion

Mastering LLM token optimization is no longer optional for serious software developers. By leveraging these six repositories, you regain absolute control over your context windows and slash your operational costs. Stop burning expensive API credits on whitespace and start optimizing your agent environments today.

Frequently Asked Questions