Open-source tools target AI agent token and cost waste

gwen-digestor is an open-source MCP server built to reduce the amount of conversation text sent into an LLM context window. It checks what kind of message it is handling and compresses it before the model sees it. The reported results are a 38.3% cut in total token use and about a 72% cut for model output responses.

It does not need a GPU, external API calls, or embeddings, because it uses fixed text rules and message structure instead. Status check-ins are turned into structured numbers, task messages lose filler, JSON is made smaller, and code has comments removed. A gzip-compressed reference cache avoids processing the same text again, and built-in stats track real savings over time.

The wider pattern is similar across related tools: coding-agent prompts, file reads, tool lists, and model-routing workflows are being compressed or simplified to cut token use, with claimed savings ranging from about 40% to over 90% in specific cases.