A Local-First Context Compression Layer for AI Agents: Library, Proxy, and MCP in One Stack
Headroom is a local-first context compression layer built specifically for AI coding agents. It slashes token consumption by 60-95% by compressing tool outputs, logs, files, and RAG results before they reach the LLM, all while maintaining answer accuracy. Usable as a Python/TypeScript library, a transparent proxy, a CLI wrapper for popular agents, or an MCP server, it fits into existing workflows without friction. Internally, it combines JSON structure-aware compression, AST-based code minification, and a custom fine-tuned model, grounded by a novel CCR reversible compression system that guarantees original data is never lost. This tool is ideal for engineers who rely heavily on coding agents and want to cut API costs without altering their current toolchain.