RAGFlow
Open-source RAG orchestration engine for document AI
Open-source RAG engine with deep document understanding, hybrid retrieval, and template-based chunking for extracting knowledge from complex formatted data.
Overall Rating
4.0
89 reviews
Features4.3
Ease of Use3.8
Support3.9
Value4.5
Performance4.1
Company Information
Founded
2024
Headquarters
Global (Open Source)
Company Size
Open Source Community employees
Pros
- Truly open-source (Apache 2.0) with 68K+ GitHub stars - vibrant community
- State-of-the-art hybrid retrieval with multiple recall + fused re-ranking
- Deep document understanding extracts knowledge from complex formats (OCR, layouts)
- Zero licensing costs - only pay for infrastructure and LLM APIs
- Complete data control - self-hosted means data never leaves your environment
- Ultimate flexibility - modify source code for any specialized requirement
- Cutting-edge features: GraphRAG, agentic workflows, code sandbox
- Fastest-growing open-source RAG project (GitHub Octoverse 2024)
- Model-agnostic: use OpenAI, local models, or custom LLMs
- Transparent architecture - audit all code and retrieval logic
Cons
- No SaaS option - requires DevOps expertise for deployment and maintenance
- Infrastructure costs can be significant for enterprise scale (servers, storage, monitoring)
- No pre-built channel integrations (Slack, Teams, WhatsApp require custom development)
- No formal compliance certifications (SOC 2, HIPAA, ISO 27001)
- Community support only - no SLA or guaranteed response times
- Steeper learning curve - developers must manage Docker, APIs, configuration
- No built-in analytics dashboard or monitoring (DIY via external tools)
- Limited no-code experience - Admin UI basic, still requires technical setup
- Role-based access control not built-in (single admin by default)
- Total cost of ownership includes engineering time for setup and maintenance
Best Use Cases
Enterprises requiring complete data sovereignty and on-premise deployment
Organizations with sensitive data that cannot use third-party SaaS
Developer teams wanting full control and customization of RAG pipeline
Companies with existing infrastructure and DevOps capabilities
Projects needing specific LLM models or embedding strategies
Research teams exploring cutting-edge RAG techniques (GraphRAG, hybrid retrieval)
Organizations with complex document formats requiring advanced parsing
Businesses wanting to avoid vendor lock-in and licensing fees
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