chopratejas/headroom
Verdict: Try todayHeadroom compresses tool outputs, logs, files, and RAG chunks before they hit LLMs, slashing token counts by 60-95% while preserving answers. It's a library, proxy, and MCP server combo that could drastically cut LLM costs.
Why builders care
Token efficiency directly impacts LLM cost and speed, making this a must-try for any builder working with RAG or LLM pipelines.Who should steal this
AI engineers optimizing LLM pipelinesGoblin build idea
Integrate Headroom into your RAG pipeline to compress context before feeding it to GPT-4 or Claude.Context is the new oil and headroom is the refinery. This Python tool squeezes tool outputs, logs, files, and RAG chunks before they hit your LLM, claiming 60-95% token reduction. It ships as a library, a proxy, and an MCP server, so you can drop it into existing agent stacks without rewriting anything. A genuinely practical answer to runaway context bills.
Why builders care
Every agent builder bleeds tokens on bloated tool returns and verbose logs. Headroom sits between your tools and the model and just makes them smaller, with the model still getting the same answers. The MCP server angle means Claude and Cursor users can wire it in with one config line.Who should steal this
Anyone running agents with expensive context, especially folks proxying MCP tool callsGoblin build idea
A headroom-plus-repoprompt combo service that watches a session's token spend in real time and visibly strips redundant fields from search results before they reach the model