ActionLib: How I Cut My Agent's Token Usage by 97%

Every AI agent framework — LangChain, AutoGen, CrewAI, you name it — has the same problem. They re-generate prompts for the same basic actions on every single call. Agent: "I need to read this file" Framework: *generates prompt explaining how to read a file* (200 tokens) Agent: "execute read_file tool with path=/tmp/foo.txt" Framework: *generates another prompt* (100 tokens) Result: 300 tokens burned on something that should cost 0 This happens for read_file , write_file , run_cmd , http_get , git_status — every action, every call, over and over. The Fix: ActionLib I built ActionLib — a standard library for AI agents. The idea is simple: Move deterministic actions out of the LLM's scope entirely Agent: "execute read_file with path=/tmp/foo.txt" ActionLib: looks up actions["read_file"], runs it locally ActionLib: returns result ← no LLM token burned This happens for read_file , write_file , run_cmd , http_get , git_status — every action, every call, over and over. The Fix: ActionLib I b