Production-Ready RAG Framework for Multi-Tenant Chatbots
Building a RAG chatbot from scratch means wiring together LangChain, a vector DB, memory management, multi-tenancy, and streaming. LongTrainer does all of this in 10 lines of code. Multi-tenant bots, persistent MongoDB memory, 9 vector DB providers, streaming, tool calling, and LangGraph agents - all batteries included.
pip install longtrainerNo more wiring together dozens of components. LongTrainer handles the full stack.
Each bot has its own documents, vector store, memory, and config. Perfect for SaaS applications where each customer needs a separate AI assistant.
FAISS, Pinecone, Chroma, Qdrant, PGVector, MongoDB Atlas, Milvus, Weaviate, and Elasticsearch. Configure per bot.
Sync and async streaming out of the box. Real-time token-by-token responses for better user experience.
Switch from RAG mode to full LangGraph agent mode with tool calling. Web search, calculators, custom APIs - any LangChain tool.
OpenAI, Claude, Gemini, AWS Bedrock, HuggingFace, Groq, Together AI, and Ollama. Configure the LLM per bot.
Built-in FastAPI server. Run `longtrainer serve` for a zero-code REST API. Docker-ready for production deployment.
We build custom RAG systems using LongTrainer for enterprise clients. Schedule a free consultation.