Headroom compresses tool output before it reaches a language model. It provides library, proxy, and MCP approaches for reducing oversized context, helping AI workflows retain more room for the information relevant to the task.
Reduce overwhelming tool responses
Process large outputs when logs, structured data, or repeated text consume too much of an agent's available context.
Improve a tool-heavy agent workflow
Explore compression around existing tools when an agent spends much of its session reading output instead of progressing through the user's task.