OpenRAG vs Ragen
OpenRAG
open-rag.aiRagen
ragen.aiSide by side
| Figure | OpenRAG | Ragen |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 2 |
| Spec score · Retrieval-Augmented Generation Tools | 8.4 (#8) | 7.6 (#19) |
| Access control | Partitions isolate knowledge bases; partition roles include owner, editor and viewer, and API tokens are stored as SHA-256 hashes. | Permissions are checked per file and folder for each answer, including answers requested through AI agents. |
| Integrations | The product documents compatibility with OpenAI API clients and names Open WebUI, LangChain, n8n and Twake.ai as integrations. | The integrations page lists Google Drive, HubSpot, ClickUp, Fireflies.ai, Google Calendar, Gmail, Slack, Notion, Google Analytics, WooCommerce, Open Mercato and Rejestr.io. |
| Model choice | The site says users can connect their own models, including Mistral, Qwen, Lucie, Claude and GPT, or use a hosted provider. | Customers can use models locally, through their own provider keys or through an OpenAI-compatible endpoint. |
| Purpose | OpenRAG is a modular framework for building document-grounded retrieval-augmented generation systems. | Ragen answers questions from a company's documents and tools, with citations to the source. |
| Security | The site describes fail-closed scopes, verified outbound connections, redacted secrets, non-root containers, rate limiting and security headers. | Conversation content can be encrypted with keys in the customer's AWS KMS or Scaleway Key Manager, and optional masking covers PESEL, NIP, REGON and card numbers. |
| Support | The product site directs technical questions to GitHub issues and links to documentation and a contact form. | Self-hosted includes deployment documentation and community support; Enterprise includes an SLA, priority support and a dedicated Customer Success Manager. |
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.
Ragen
Ragen is a company knowledge assistant available through web, API, or self-hosting. It answers questions using company documents and connected tools, and includes citations to the source material. Teams can create separate assistants for departments such as HR, sales, compliance, customer service, and operations. A website chatbot handles product and policy questions continuously and can be embedded on permitted domains with one line of code. Ragen exposes knowledge to agents through MCP, an OpenAI-compatible API, and a TypeScript SDK. Integrations listed include Google Drive, HubSpot, ClickUp, Fireflies.ai, Google Calendar, Gmail, Slack, Notion, Google Analytics, WooCommerce, Open Mercato, and Rejestr.io; BaseLinker and KSeF are marked coming soon. Ragen Brain organizes documents into knowledge pages, flags contradictions and outdated files, and requires human approval before publishing. File and folder permissions are checked for every answer. Data remains on the customer’s infrastructure, with optional encryption keys and masking for specified identifiers.