Headroom

Headroom is a context optimization layer for LLM applications and coding agents. It compresses what an agent reads, including tool outputs, database results, file reads, RAG retrievals and API responses, before the content reaches the model, so the model sees less noise, responds faster and costs less. It is for developers building agents and AI features who want to cut token use without changing answers. Compression is content aware: JSON arrays are analyzed statistically so errors and anomalies are kept, build and test logs keep failures and drop passing noise, search results are ranked, plain text is reduced with ModernBERT token classification, and git diffs keep change hunks. Source code compression is AST aware but opt in. A reversible compression mode lets the model retrieve original content when it needs it. It also offers cache optimization, persistent memory, a proxy server, savings tracking, and integrations with the Vercel AI SDK, OpenAI and Anthropic SDKs, LangChain, CrewAI, AutoGen, LiteLLM, Claude Code and VS Code Copilot, with TypeScript and Python APIs. The documentation lists a Headroom for Teams offering, and the site currently advertises a promotion on 12 month plans. No plan prices are published in the documentation, so none are quoted here.