Headroom is an open-source context optimization layer that compresses everything an AI agent reads — tool outputs, logs, files, database results and RAG chunks — before it reaches the language model. The project has collected more than 76,000 stars on GitHub and is under very active development, with its latest updates in 2026. Its promise: the same answers with a fraction of the tokens, so agents respond faster and cost less.
Headroom sits between your application and the LLM provider as a transparent proxy, a Python or TypeScript library, or an MCP server. It detects the content type automatically and routes it to a specialized compressor: JSON arrays are analyzed statistically so errors and anomalies survive, logs keep their failures and drop passing noise, and code is compressed with AST awareness. A Compress-Cache-Retrieve store keeps the originals, giving the model a tool to fetch full details when compression went too far — aggressive, but reversible. Newer releases add persistent hierarchical memory and failure learning, which reads past sessions, finds failed tool calls and writes the lessons back into project instructions.
Headroom is free and open source under the Apache-2.0 license. You can self-host the proxy or embed the libraries in your own stack at no cost; your only spend remains your existing LLM API bill — which Headroom is designed to shrink.
Headroom is for developers running agentic applications in production — coding assistants, support agents, research tools — whose context windows are flooded with tool output and whose token bills reflect it.