ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
RAGFlow MCP server provides AI agents with access to a sophisticated Retrieval-Augmented Generation engine that combines document understanding, chunking, and context retrieval capabilities. It enables agents to query knowledge bases built from diverse document types (PDFs, Word files, web pages, images) with grounded citations and reduced hallucinations. Developers can integrate RAGFlow's deep document parsing and template-based chunking into their AI workflows through the Model Context Protocol, allowing agents to retrieve high-fidelity contextual information from complex unstructured data sources.
Key Features
Use Cases
- 01Building AI agents that need to answer questions from enterprise document repositories with verifiable sources
- 02Creating chatbots that retrieve accurate information from technical documentation and manuals
- 03Developing research assistants that analyze and cite information from academic papers and reports
- 04Implementing customer support agents that query product documentation and knowledge bases
- 05Building compliance tools that extract and reference specific clauses from legal and regulatory documents
- 06Creating data analysis workflows that process and understand scanned documents and images
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ragflow — FAQ
What is the RAGFlow MCP server?+
RAGFlow MCP server is an interface that connects the RAGFlow Retrieval-Augmented Generation engine to AI agents via the Model Context Protocol. It enables agents to access deep document understanding, intelligent chunking, and grounded retrieval capabilities for complex unstructured data.
How do I install and run RAGFlow for MCP integration?+
RAGFlow requires Docker (24.0.0+), Docker Compose (v2.26.1+), and at least 4 CPU cores with 16 GB RAM. Clone the repository, configure vm.max_map_count to at least 262144, then use docker compose to start the server. Access the web interface to configure your LLM API keys and create knowledge bases.
What are the prerequisites for using RAGFlow?+
You need Docker and Docker Compose installed, a machine with at least 4 CPU cores and 16 GB RAM, and API keys for your chosen LLM provider (OpenAI, Gemini, DeepSeek, etc.). Configure the API keys in service_conf.yaml.template after deployment.
Which AI clients and platforms work with RAGFlow MCP?+
RAGFlow supports MCP integration and can connect to various AI agent frameworks. It also provides chat channels for Feishu, Discord, Telegram, and Line, and offers an official skill for OpenClaw.
Is RAGFlow free to use?+
Yes, RAGFlow is open-source software licensed under Apache 2.0 and free to self-host. However, you will need to provide your own API keys for LLM and embedding model services, which may have associated costs from the providers.
Can RAGFlow handle different document formats?+
Yes, RAGFlow supports Word, PowerPoint, Excel, TXT, images, scanned copies, structured data, web pages, and more. It uses deep document understanding to extract knowledge from complex formats with high fidelity.
How do I install ragflow?+
Open the source repository on GitHub and follow its README. ragflow is a mcp server — MCP Agents Market links you directly to the official repo.
Is ragflow free?+
ragflow is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.