Adding an LLM to your application usually means writing the same infrastructure over and over: define JSON schemas for each tool, dispatch tool calls, drive the agentic loop, wire up a vector store, manage embedding calls, handle session state. Before you know it the actual feature is buried under plumbing. Daimon is a Go sidecar that takes a different approach. Drop the binary next to your app, write a YAML config, and you get a fully operational LLM endpoint — with vector search, graph queries, session memory, and a complete agentic loop — without writing any of that wiring yourself. The key idea: when you declare a vector store or graph database in the config, Daimon auto-generates LLM tools for it ( {name}_search , {name}_cypher , etc.) and injects them into every LLM call. The model can use them immediately. You named the component; you got the tools. To show what this looks like end-to-end, we'll build a medical chart ICD-10 coding pipeline.…