Headroom
Headroom
Context optimization layer for LLM apps: compresses what agents read (tool outputs, logs, RAG, file reads) while preserving accuracy.
Links
- GitHub: https://github.com/chopratejas/headroom (1.5k ⭐, Apache-2.0)
- Docs: https://headroom-docs.vercel.app/docs
Overview
Headroom strips boilerplate and keeps signal — losslessly and locally — before context reaches the model. It targets high-volume agent inputs like JSON tool outputs, build/test logs, search results, diffs, and source code.
How it fits (one line)
Agent/App → (tool outputs, logs, DB reads, RAG chunks, files) → Headroom → LLM provider.
Notable features
- Wrap existing coding agents:
headroom wrap claude|codex|cursor|aider|copilot - Proxy mode: run as a local proxy for zero-code integration
- Content-aware compressors: JSON, code (AST-aware), logs, text, diffs, search results, images
- CCR (reversible / lossless compression): compress aggressively but allow retrieval of originals
- Local-first: no data egress; millisecond compression latency
- Cross-agent memory + learning: mines failed sessions and can write corrections to CLAUDE.md / AGENTS.md / GEMINI.md
Numbers (as marketed)
- ~87% token reduction on example log debugging (10,144 → 1,260) with identical answer.