Your AI agent is drowning
in CLI noise. Fix it.
rtk compresses command outputs before they reach the context window. Better reasoning. Longer sessions. Lower costs.
Why RTK? The numbers.
89% noise reduction measured across 2,900+ real-world dev commands: cargo test 91.8%, git status 80.8%, find 78.3%, grep 49.5%. Free, open source (MIT, Rust).
📊 RTK Token Savings
════════════════════════════════════════
Total commands: 2,927
Input tokens: 11.6M
Output tokens: 1.4M
Tokens saved: 10.3M (89.2%)
By Command:
────────────────────────────────────────
Command Count Saved Avg%
rtk find 324 6.8M 78.3%
rtk git status 215 1.4M 80.8%
rtk grep 227 786.7K 49.5%
rtk cargo test 16 50.1K 91.8%
$
- Claude Code
- Cursor
- Aider
- Gemini CLI
- OpenAI Codex
- Cline
- Windsurf
- GitHub Copilot
One forge. Three tools.
RTK, ICM, and Vox share one philosophy: open source, Rust, zero telemetry, local-first.
Commands, file reads, tests — compressed 60-90% before they reach your model context. Zero config.
89% noise removedPersistent memory across sessions. Your agent picks up where it left off — decisions, errors, context.
Infinite context memoryVoice output for your AI agent. Three TTS backends, four Claude Code integration modes.
3 TTS backends
The problem with AI coding today
Every command your agent runs pollutes the context window with noise. Here's what that costs you.
Context pollution
Your 200K context window isn't infinite. When cargo test dumps 5,000 tokens of boilerplate, that's 5,000 tokens less for reasoning about your actual code.
context_quality: degraded ▼ Sessions too short
Context overflows, the agent restarts, you lose the thread. On flat-rate plans, you hit rate limits 40% faster than you should.
session_remaining: 32% ▼ Costs that explode
On pay-per-token setups (API, Gemini CLI, Aider), 70% of your bill is noise the LLM doesn't need. A team of 10 wastes ~$1,750/month.
token_waste: $1,750/mo ▲
See the difference
Real outputs, real savings. Side-by-side comparison on actual commands.
Real-world savings
Actual rtk gain output from a happy developer.
A developer's feedback
After a few weeks of daily use: 15,720 commands processed, 138M tokens saved. Just run rtk gain to see yours.
Detailed breakdown
Daily, weekly, and monthly stats by command. Track your savings over time.
Per-command analytics
No AI tool offers unlimited usage. Every token counts.
Even at $200/mo, every tool has caps. RTK compresses CLI noise so your limits stretch further.
A typical 2h coding session with an AI agent:
Without RTK, CLI output alone can overflow a 200K context window. Based on avg 3,500 tokens/command measured across 2,900+ real commands.
Pricing verified Feb 2026. Limits vary by usage and plan. RTK savings based on avg 89% compression across 2,900+ real commands.
RTK Cloud
Visibility and control over your team's AI coding costs. Know what's wasted. Fix it.
Token analytics
Dashboard per dev, per project, per tool
Team savings reports
"Your team saved $4,200 this month"
Rate limit alerts
Monitoring & smart notifications
Enterprise controls
SSO, audit logs, compliance
Free for open-source. Teams from $15/dev/month.
0 teams on the waitlist
No spam. One email when we launch.
Get started in 30 seconds
Install, activate the auto-rewrite hook, and every command is compressed automatically.
Quick Install
One-liner for Linux & macOS
curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh Via Homebrew
macOS & Linux
brew install rtk brew upgrade rtk Then activate the auto-rewrite hook
rtk init --claude-code Installs PreToolUse hook in Claude Code settings.json — every Bash call is rewritten automatically. Full install guide →
rtk init --cursor Configures Cursor's .cursorrules to pipe Bash commands through rtk. Full install guide →
rtk init --global Installs a global shell hook — works with Aider, Gemini CLI, Codex, Windsurf, and any terminal AI tool. Full install guide →
curl ... | sh rtk init --claude-code rtk gain Your AI doesn't need
to read all that.
Install rtk. Better code, longer sessions, lower costs.
Frequently asked questions
What is RTK (Rust Token Killer)?
RTK is an open-source CLI tool that compresses command outputs before they reach the AI context window. It reduces token usage by 60-90% with zero configuration changes, enabling longer AI coding sessions and lower API costs. RTK is written in Rust, MIT licensed, and works transparently with Claude Code, Cursor, and any terminal-based AI assistant.
How many tokens does RTK actually save?
Based on measurements across 2,900+ real-world commands, RTK removes an average of 89% of CLI output noise. Command-level savings: cargo test (91.8% savings), git status (80.8%), find (78.3%), grep (49.5%). A developer who ran 15,720 commands saved 138 million tokens over several weeks, tracked live via rtk gain.
Is RTK free? Are there usage limits?
RTK is completely free. It is open source under the MIT license, with source code available on GitHub at github.com/rtk-ai/rtk. There are no usage limits, no API keys required, no telemetry, and no accounts. RTK Cloud (waitlist) will offer additional team features.
Which AI coding tools and commands does RTK support?
RTK works with Claude Code (Anthropic), Cursor, Gemini CLI, Aider, and any AI assistant that reads terminal output. It supports all major CLI commands: cargo test, pytest, go test, git diff, git status, git log, grep, find, ls, pnpm list, tsc, eslint, prisma, docker, kubectl, and more.
How does RTK work without changing my workflow?
Running rtk init --global installs a PreToolUse hook in Claude Code that automatically rewrites Bash commands to rtk equivalents at the proxy layer. You continue using your normal commands. RTK intercepts and compresses the output before it enters the context window, with no manual changes to prompts or workflows needed.
Does RTK affect code quality or AI reasoning accuracy?
No. RTK removes verbose boilerplate and repetitive output noise, not meaningful content. Test failures, error messages, diffs, and stack traces are preserved in full. The AI receives the same essential information with 89% less noise, which typically improves reasoning quality by reducing context pollution.