headroomlabs-ai/headroom
Many AI systems waste resources by processing unnecessary data.
The Headroom library addresses this by compressing tool outputs, logs, and RAG chunks before they reach the LLM. It can reduce token usage by 60% to 95% while maintaining the same quality of answers.
For those working in the public sector, this matters because it focuses on cost-effective scaling. Instead of simply feeding more data into a system, it emphasizes smarter context. This approach helps make AI infrastructure more sustainable and easier to manage at scale.
How are you managing token costs in your current workflows?
#AI #PublicSector #RAG #LLM #TechEfficiency
https://github.com/headroomlabs-ai/headroom