A MitM proxy to see what your LLM tools are sending
I built this out of curiosity about what Claude Code was actually sending to the API. Turns out, watching your tokens tick up in real-time is oddly satisfying. Sherlock sits between your LLM tools and the API, showing you every request with a live dashboard, and auto-saved copies of every prompt as markdown and json.
A MitM proxy to see what your LLM tools are sending launched on January 28, 2026 with 218 votes, #125 of 14,584 launches that month and more than 99% of that year's launches.
In plain words
Sherlock is a man-in-the-middle proxy that intercepts and displays all API requests between LLM tools and their services. It provides a live dashboard showing real-time token usage and automatically saves copies of every prompt in both markdown and JSON formats. The tool is designed for developers and researchers who want visibility into what data their LLM applications are sending and how they're using their API quota.
written from the facts on this page · September 2026
From the sources
Intercept LLM API traffic and visualize token usage in a real-time terminal dashboard. Track costs, debug prompts, and monitor context window usage across your AI development sessions. - GitHub - jmuncor/tokentap: Intercept LLM API traffic and visualize token usage in a real-time terminal dashboard. Track costs, debug prompts, and monitor context window usage across your AI development sessions.
tokentap tracks token usage for LLM CLI tools with a live terminal dashboard. See exactly how many tokens you're using in real-time. git clone https://github.com/jmuncor/tokentap.git cd tokentap pip install -e . Requirements Python 3.10+ Quick Start Terminal 1: Start the Dashboard tokentap start You'll be prompted to choose where to save captured prompts, then the dashboard appears: ┌─────────────────────────────────────────────────────────────┐ │ TOKENTAP - LLM Traffic Inspector │ ├─────────────────────────────────────────────────────────────┤ │ Context Usage ████████████░░░░░░░░░░░░░░░░ 42% │ │ (84,231 / 200,000 tokens) │ ├─────────────────────────────────────────────────────────────┤ │…from github.com
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