If you've been watching the local-AI space lately, you've probably seen OpenClaw land 100k GitHub stars on the back of autonomous agents that build their own tools, their own social networks, and — if you're not careful — their own threat models. AutoBot takes a different approach: you stay in control . Your data never leaves your machine. Your AI runs on your hardware. And the knowledge base — the thing that makes your local AI actually useful — is something you can read, extend, and contribute to. This post is for Python developers who want to understand exactly how that knowledge base works, how to feed it your own codebase, and where to plug in if you want to help build it. The Stack at a Glance AutoBot's RAG pipeline is built on three components: Layer Technology Role Embedding model Ollama (configurable) Text → vectors Vector store ChromaDB Similarity search Retrieval + generation LlamaIndex Query → answer All of it runs locally. No API calls. No data leaving your machine.…