Ranked by Attention Score: stars gained over the last 90 complete days
4 repositories by Attention Score · Aug 5, 2026 – Nov 2, 2026 (UTC)
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Cut AI context cost without trusting the compressor. Every reduction is reversible, byte-exact recoverable, and carries an auditable receipt. Local-first, works through proxy, MCP, SDK, or agent wrapper.
Cut context bloat in your AI-agent stack: find and safely prune unused skills, MCP servers and subagents from real transcript evidence