Overview

RTK (Rust Token Killer) is a high-performance CLI proxy designed to dramatically reduce LLM token consumption. By intelligently filtering and compressing the output of common developer commands before they reach your AI assistant's context, RTK slashes token usage by an impressive 60-90%. This single Rust binary boasts zero external dependencies and minimal overhead (<10ms per command), making it an essential tool for cost-effective and efficient AI-assisted development.

Key Features

• Massive Token Savings: Achieves 60-90% reduction in LLM token consumption across a wide array of development commands, including git, cargo, npm, pytest, docker, aws, and more.
• Intelligent Filtering & Compression: Employs strategies like smart filtering, output grouping, truncation, and deduplication to condense command output without losing crucial context.
• Zero Dependencies & Single Binary: Distributes as a single, self-contained Rust binary, simplifying installation and eliminating dependency conflicts.
• Low Overhead: Operates with less than 10ms of additional latency per command, ensuring a seamless developer experience.
• Extensive Command Support: Optimizes over 100 common developer commands, with ongoing expansion.
• Seamless AI Integration: Features an optional auto-rewrite hook that transparently intercepts and modifies commands for AI tools like Claude Code, Copilot, and Gemini CLI.
• Detailed Analytics: Provides tools like rtk gain to visualize token savings and identify potential optimization opportunities.

Typical Use Cases

• Cost Reduction: Significantly lowers the operational costs associated with using LLMs for code-related tasks by minimizing token usage.
• Enhanced LLM Performance: Allows LLMs to process more relevant information within their context window, potentially leading to more accurate and efficient code generation and analysis.
• Streamlined Developer Workflows: Integrates effortlessly into existing command-line workflows, automatically optimizing output for AI interactions without manual intervention.
• Debugging & Analysis: Provides condensed and focused output for command results, making it easier for developers and AI assistants to identify issues or understand system states.
• Automated Code Review & CI/CD: Enables AI agents to more effectively participate in code review processes or analyze build/test outputs with reduced token costs.