Build multi-tenant AI chatbots with persistent memory, streaming, tool calling, and 9 vector DB providers — in 10 lines of Python.

The RAG Boilerplate Problem Nobody Talks About

Every developer building a production RAG chatbot eventually faces the same wall.

You start with a LangChain tutorial. You connect an LLM. You load a PDF. You get a response. It works — and then reality hits.

You need multiple bots for multiple customers. You need their conversation history to survive a server restart. You need real-time streaming responses. You need your bot to call external APIs when documents don’t have the answer. You need to store vectors somewhere other than RAM. You need encryption.…