Headroom: Open-Source Tool to Compress Tool Outputs, Logs, Files, and RAG Chunks by 60-95% Tokens Without Quality Loss

Headroom, developed by headroomlabs-ai, focuses on compressing tool outputs, logs, files, and RAG chunks before LLM input. It can reduce token count by 60-95% while maintaining answer quality. The project offers three integration methods: Library, Proxy, and MCP Server, allowing flexible adoption in existing workflows. For developers using Claude API, Anthropic models, or running agents, this tool directly lowers per-request token costs, especially when handling large logs or RAG retrieval results. The MCP Server support enables seamless integration with tools like Claude Code, reducing bandwidth and costs. The project has gained over 56,000 GitHub stars, with 2,408 added in the last seven days, indicating strong community interest. For teams managing long contexts or large data inputs, Headroom provides a lightweight and effective compression solution.