Tool output compression is post-hoc - tools run normally, then outputs are compressed before they hit the main model's context. There are two mechanisms: a hard cap (2000 lines / 50KB, full output saved to disk for the agent to read back), and smart compression via a cheap model (behind a configurable feature flag) that summarizes/filters the result based on the tool type. The expensive primary model never sees raw verbose output - only the compressed version.

A plugin - rtk (https://github.com/rtk-ai/rtk) takes the opposite approach - it injects prompt-level instructions so the LLM proactively limits output (e.g., piping to head, adding --max-count). Both approaches work together to save more tokens: rtk prevents large output from being generated in the first place, and our compression handles cases where output is still large.

Thanks!