Overview
RAGFlow (88k+ stars, Apache-2.0) is an enterprise-grade open-source RAG engine with deep document understanding and Agent capabilities, deployable via Docker. MaxKB (22k+ stars, GPL-3.0) is an open-source knowledge base Q&A and Agent-building platform focused on zero-code integration and workflow orchestration. We compare retrieval capabilities, document processing, deployment, and typical scenarios.
Go · Apache-2.0
A leading open-source RAG engine that fuses cutting-edge retrieval-augmented generation with agent capabilities to create a superior context layer for LLMs.
Python · GPL-3.0
MaxKB is an open-source knowledge base Q&A and agent building platform powered by LLMs, with vector retrieval, workflow orchestration, and multi-model support out of the box.
| Best for | RAGFlow | MaxKB |
|---|---|---|
| Product positioning | Open-source RAG engine focused on deep document understanding, knowledge extraction, and Agent capabilities. Enterprise-grade, "a superior context layer for LLMs." | Open-source knowledge base Q&A and Agent-building platform with zero-code integration, workflow orchestration, and MCP tool-use. "Build enterprise-grade agents quickly." |
| Retrieval capabilities | Multi-path recall with fused re-ranking. Supports multiple embedding models and LLM configurations. Template-based chunking with strong explainability. | Hybrid vector + full-text search. Automatic text splitting and vectorization. Multi-turn conversation context support. |
| Document processing | DeepDoc: supports Word, PPT, Excel, TXT, images, scanned documents, structured data, and web pages. Template-based chunking with traceable citations. | Supports document upload and automatic crawling. Multi-modal (text, image, audio, video). Zero-code integration with third-party systems. |
| Deployment | Docker Compose one-click deployment. GPU acceleration for DeepDoc tasks. Managed cloud service at cloud.ragflow.io. Main service Python (ragflow_server.py); Go is used for sandbox executor. | Single Docker command to start. Offline installation supported. Python / Django backend + Vue.js frontend. PostgreSQL + pgvector. |
| Best fit | Enterprise RAG applications, complex document understanding, compliance scenarios needing traceable citations, teams with existing Docker infrastructure. | Quick internal knowledge base setup, zero-code Agent building, small-to-medium teams, scenarios needing multi-modal support. |
| Metric | RAGFlow | MaxKB |
|---|---|---|
| Stars | 89.9k | 22.7k |
| Forks | 10.6k | 3.1k |
| Language | Go | Python |
| License | Apache-2.0 | GPL-3.0 |
| Last commit | September 2, 2026 | September 2, 2026 |
Choose based on your primary workflow, language ecosystem, and integration needs. Review each project's documentation and recent GitHub activity before adopting it in production.
Both are Chinese-friendly. RAGFlow has DeepDoc for extracting text from Chinese scanned documents and PDFs. MaxKB supports multi-modal input for Chinese knowledge base Q&A.
MaxKB is simpler: a single Docker command. RAGFlow needs Docker Compose but has good documentation. For non-technical users, MaxKB is faster to get started.
Both support multiple LLM and embedding models. RAGFlow's backend can connect to the LangChain ecosystem. MaxKB has built-in LangChain framework for more direct integration.
RAGFlow scales to enterprise-grade RAG (88k stars, Apache-2.0, GPU acceleration). MaxKB suits small-to-medium teams building knowledge bases quickly (22k stars, GPL-3.0).