headroom

What it does

Headroom is a token compression tool designed to optimize LLM context windows by intelligently compressing tool outputs, logs, files, and RAG chunks before they reach language models. It reduces token consumption by approximately 20% for coding agents and 60-95% for JSON data while preserving output quality and correctness. The tool helps maximize context utilization and reduce API costs across multiple LLM providers.

How to set up

Headroom offers three flexible deployment options: (1) as a Python library that can be imported directly into your project, (2) as a proxy server that intercepts and compresses requests, or (3) as an MCP (Model Context Protocol) server for direct integration with Claude and other compatible tools. Installation typically involves cloning the repository, installing dependencies via pip, and configuring compression parameters based on your use case.

Key features

• Multi-format compression supporting logs, JSON, files, and RAG chunks
• Dramatic token reduction: 20% for code, 60-95% for JSON
• Three deployment modes: library, proxy, and MCP server
• Compatible with multiple LLM providers including Claude and OpenAI
• Integration with popular frameworks like LangChain and FastAPI
• Context window optimization for improved agent performance