OpenRAG vs RAGFlow
OpenRAG
open-rag.ai
RAGFlow
ragflow.ioSide by side
| Figure | OpenRAG | RAGFlow |
|---|---|---|
| Price from | Free | $29/mo |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 5 |
| Spec score · Retrieval-Augmented Generation Tools | 8.4 (#8) | 8.8 (#3) |
| License | OpenRAG is licensed under AGPL-3.0. | The public repository is labeled Apache-2.0 licensed. |
| Purpose | OpenRAG is a modular framework for building document-grounded retrieval-augmented generation systems. | RAGFlow describes itself as an open-source RAG engine and integrated agent platform for building a context layer for AI agents. |
| Search | Retrieval combines semantic search with BM25 keyword matching and multilingual reranking. | Its search combines vector search, BM25, custom scoring, and advanced re-ranking. |
| Security | The site describes fail-closed scopes, verified outbound connections, redacted secrets, non-root containers, rate limiting and security headers. | By default, RAGFlow validates MCP server URLs and rejects hosts that resolve to loopback, private, link-local, reserved, or other non-public addresses. |
Plans
OpenRAG
- OpenRAG (AGPL-3.0 self-hosted)Free
- Managed OpenRAG servicePriced on request
RAGFlow
- FreeFree
- FreeFree
- Starter$29 a month
- Pro$129 a month
- EnterprisePriced on request
In their own words
OpenRAG
OpenRAG is a modular framework for building systems that answer questions using documents as their grounding. Its retrieval combines semantic search with BM25 keyword matching and multilingual reranking; answers cite a source document and page, with links to the cited location. Document processing covers listed text, office, email, audio, video, and image formats, with PDF layout awareness, OCR, image captioning, and audio transcription. The product includes an admin console, chat interface, and OpenAI-compatible API, and can connect to user-provided models or a hosted provider. Partition isolation, owner, editor, and viewer roles, plus token or OpenID Connect authentication, are among its access controls. It can run on infrastructure controlled by the deployer, keeping documents, embeddings, and queries within that environment. The self-hosted AGPL-3.0 edition is listed at free. Deployment prerequisites are Docker, Docker Compose, and at least 16 GB of RAM; a CPU-only profile is optional. CSV, ODT, and HTML support, tool calling, agentic RAG, MCP, and encryption in transit and at rest are listed as coming soon.
RAGFlow
RAGFlow is an open-source retrieval-augmented generation engine and agent platform for building a context layer for AI agents. Its ingestion pipeline processes multiple data formats into semantic representations for retrieval. Search combines vector search and BM25 with custom scoring and re-ranking, while visual workflows connect retrieval, tools, and MCPs for agent orchestration. Users can connect online, local, and OpenAI-compatible model providers for knowledge bases, chats, search, and agents. Documented connectors include Notion, Google Drive, S3, Microsoft Teams, REST API, and Sitemap. RAGFlow provides HTTP and Python APIs, and a chat assistant can be embedded in another webpage using an iframe. It offers cloud and self-hosted deployment; Docker deployment has a stated minimum of 4 CPU cores, 16 GB RAM, and 50 GB disk. The free plan includes five apps, one team member, 0.1 GB of dataset storage, and 500 monthly credits, but no API key. Starter costs $29 a month and includes an API key.