knowledge-rag vs MaxKB
knowledge-rag: Knowledge RAG is a fully local, zero-cloud document retrieval system that lets Claude Code search your PDFs, markdown, code, and notebooks natively. It indexes up to 1800+ files in under 3 minutes, using hybrid search (BM25 + semantic vectors + cross-encoder reranking) via 12 MCP tools. Everything runs locally via ONNX, no Docker, Ollama, or API keys required.; MaxKB: MaxKB is an open-source platform for building enterprise-grade AI agents with integrated RAG pipelines, workflow orchestration, and MCP tool-use capabilities. It supports direct document upload, automatic web crawling, and multiple embedding models for knowledge base construction. Widely used for intelligent customer service, internal knowledge bases, and academic research applications.
TL;DR
Personal document retrieval and knowledge base for developers
- You want an all-in-one knowledge-base and workflow agent platform that starts with a single Docker command and embeds easily into websites.
- You need broad support for domestic Chinese LLM providers alongside global models for internal enterprise Q&A.
- You cannot use GPL-3.0 licensed software in your commercial distribution; RAGFlow is Apache-2.0.
- Your primary bottleneck is extracting complex tables and layouts from messy scanned PDFs; RAGFlow's deep document parser goes further.
Side-by-Side Comparison
Features
Use Cases
Best For
FAQ
Deploy on DigitalOcean
Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.