Langflow vs OpenRAG
Langflow
langflow.orgOpenRAG
open-rag.aiSide by side
| Figure | Langflow | OpenRAG |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 2 |
| Spec score · LLM Application Development Frameworks | 7.6 (#11) | 8.3 (#5) |
| Spec score · Retrieval-Augmented Generation Tools | 7.5 (#20) | 8.4 (#8) |
| Integrations | The homepage lists integrations including Anthropic, Azure, OpenAI, Hugging Face, Ollama, Qdrant, Pinecone, Slack, and Google Drive. | The product documents compatibility with OpenAI API clients and names Open WebUI, LangChain, n8n and Twake.ai as integrations. |
| Model choice | Langflow does not require users to use specific large language models or vector stores. | The site says users can connect their own models, including Mistral, Qwen, Lucie, Claude and GPT, or use a hosted provider. |
| Purpose | Langflow is an open-source, Python-based framework for building customizable AI applications, including agent and MCP applications. | OpenRAG is a modular framework for building document-grounded retrieval-augmented generation systems. |
| Security | Langflow can execute developer-provided Python with access to the host backend, filesystem, and network, and it does not enforce user isolation within a single process. | The site describes fail-closed scopes, verified outbound connections, redacted secrets, non-root containers, rate limiting and security headers. |
| Support | The official site links to community help on Discord, professional services, and Premier Support. | The product site directs technical questions to GitHub issues and links to documentation and a contact form. |
Plans
OpenRAG
- OpenRAG (AGPL-3.0 self-hosted)Free
- Managed OpenRAG servicePriced 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.