chopratejas/headroom

Headroom sits between your data sources and your LLM calls and compresses the payload. Logs, RAG chunks, file contents, tool outputs — anything that tends to bloat context gets squeezed before the model sees it. The stated range is 60-95% fewer tokens with no meaningful loss in answer quality, which at current API prices translates directly to a lower monthly bill and faster round-trips. What makes this one worth clicking is the delivery mechanism: it is not just a library you have to plumb into your own pipeline. It also ships as a drop-in proxy and as an MCP server, which means you can wire it into an agentic setup without touching your core app logic. The proxy path in particular is the kind of thing that should take a Saturday afternoon to evaluate, not a week. Honest reservation: the 60-95% claim is a wide range, and real-world compression on structured data like SQL output or typed JSON tends to sit at the low end. Test on your own payloads before committing. -> Best for: AI engineer or solo founder running LLM-heavy features at a price point where token costs actually show up on the card statement.