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. You need a REST API so the frontend team can actually use this thing. What started as a weekend prototype turns into hundreds of lines of infrastructure glue — and none of it is the actual product you are building. This is the problem LongTrainer was designed to solve. What Is LongTrainer?…