← Discover
Discover · GitHub · Headroom

Analyze your AI outputs

Headroom reduces token usage by 60% to 95% by compressing log files, tool outputs, and contextual data chunks (RAG chunks) sent to large language models (LLM). This Python-based tool offers different integration options as a library, proxy and Model Context Protocol (MCP) server.

What you get

  • Reduces coin usage by 60% to 95%.
  • Protects privacy by compressing data locally.
  • Provides recallable compression without losing original data.

Installation

Package Installation
pip install "headroom-ai[all]"          # Python
npm install headroom-ai                 # Node / TypeScript

Running it

Mode Selection and Startup
headroom wrap claude                    # wrap a coding agent
headroom proxy --port 8787              # drop-in proxy, zero code changes
Performance Control
headroom perf

If you don't write code

🤖 Paste this into your AI agent (Claude Code · Codex · Antigravity)

I want to optimize my AI agent's consumption of contextual data and log files using the Headroom tool. I completed the installation with the command "pip install "headroom-ai[all]"" in the Python environment. How should I configure the "headroom wrap claude" or "headroom proxy --port 8787" commands to reduce the amount of tokens my agent uses? Also, how should I interpret the savings data I get with the "headroom perf" command?

Related dictionary terms

RAG ChunksProxyTokenRAGMCPLLM
Who it is forIt is suitable for developers who use AI coding agents on a daily basis and want to reduce token costs.
LicenseApache-2.0

Links

TreScout did not build this tool · we found it in GitHub trends and wrote it up. This page describes the repository as of 2026-06-03: The star count and our text belong to that day, the repository may have changed since. Check the repository link for the current state. This page was machine-translated from the Turkish original · the Turkish version prevails.