RAGFlow is an open-source Retrieval-Augmented Generation engine written in Go that combines RAG technology with Agent capabilities to create a context layer for large language models.
The platform addresses the challenge of transforming unstructured data into production-ready AI systems through deep document understanding and intelligent chunking. RAGFlow supports diverse data formats including Word documents, slides, Excel files, text, images, scanned copies, structured data, and web pages. The system implements template-based chunking that is both intelligent and explainable, with visualization capabilities that allow human intervention in the text chunking process. This approach aims to reduce hallucinations through grounded citations and traceable references that support answer verification.
Key technical features include multiple recall mechanisms paired with fused re-ranking, configurable language models and embedding models, and an automated RAG workflow designed for both individual users and enterprise-scale deployments. The platform recently added support for multiple chat channels including Feishu, Discord, Telegram, and Line, along with data synchronization capabilities from sources like Confluence, S3, Notion, Discord, and Google Drive. Recent updates show integration with advanced models such as DeepSeek v4 and Gemini 3 Pro, support for agentic workflows with MCP protocol, and a Python and JavaScript code executor component for agents.
Self-hosting requirements include a minimum of 4 CPU cores, 16 GB RAM, and 50 GB disk space, with Docker 24.0.0 and Docker Compose v2.26.1 as dependencies. The system uses pre-built Docker images for x86 platforms, with the latest stable version being v0.26.3. The platform offers both a cloud service at cloud.ragflow.io and comprehensive documentation for development and deployment. RAGFlow's architecture emphasizes streamlined orchestration with configurable components, making it adaptable to different organizational scales while maintaining focus on data quality and citation accuracy in generated responses.
