open-webui vs quivr
Side-by-side comparison of two AI agent tools
open-webuifree
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
quivrfree
Opiniated RAG for integrating GenAI in your apps π§ Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore:
Metrics
| open-webui | quivr | |
|---|---|---|
| Stars | 129.4k | 39.1k |
| Star velocity /mo | 3.1k | 67.5 |
| Commits (90d) | β | β |
| Releases (6m) | 10 | 0 |
| Overall score | 0.7998995088287935 | 0.4264472901167716 |
Pros
- +Multi-provider AI integration supporting both local Ollama models and remote OpenAI-compatible APIs in a single interface
- +Self-hosted deployment with complete offline capability ensuring data privacy and security control
- +Enterprise-grade user management with granular permissions, user groups, and admin controls for organizational deployment
- +LLM-agnostic design supporting multiple providers (OpenAI, Anthropic, Mistral, Gemma) with unified API
- +Extremely simple setup requiring only 5 lines of code to create a working RAG system
- +Flexible file format support with extensible parsers for PDF, TXT, Markdown and custom document types
Cons
- -Requires technical expertise for initial setup and maintenance of Docker/Kubernetes infrastructure
- -Self-hosting demands dedicated server resources and ongoing system administration
- -Limited to local deployment model, lacking the convenience of managed cloud AI services
- -Python-only implementation limiting cross-platform development options
- -Requires Python 3.10 or newer, excluding older Python environments
- -Still actively developing core features, indicating potential API instability
Use Cases
- β’Enterprise organizations deploying private AI assistants with strict data governance and user access controls
- β’Development teams building local AI workflows with multiple model providers while maintaining code and data privacy
- β’Educational institutions providing students and faculty with controlled AI access without external data sharing
- β’Integrating document Q&A capabilities into existing Python applications without building RAG from scratch
- β’Building personal knowledge management systems that can query across multiple document formats
- β’Creating AI-powered customer support tools that can answer questions from company documentation