promptfoo vs quivr
Side-by-side comparison of two AI agent tools
promptfooopen-source
Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and
quivrfree
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore:
Metrics
| promptfoo | quivr | |
|---|---|---|
| Stars | 18.9k | 39.1k |
| Star velocity /mo | 1.7k | 67.5 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 0 |
| Overall score | 0.7957593044797683 | 0.4264472901167716 |
Pros
- +Comprehensive testing suite covering both performance evaluation and security red teaming in a single tool
- +Multi-provider support with easy comparison between OpenAI, Anthropic, Claude, Gemini, Llama and dozens of other models
- +Strong CI/CD integration with automated pull request scanning and code review capabilities for production deployments
- +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 API keys and credits for multiple LLM providers, which can become expensive for extensive testing
- -Command-line focused interface may have a learning curve for teams preferring GUI-based tools
- -Limited to evaluation and testing - does not provide actual LLM application development capabilities
- -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
- •Automated testing and evaluation of prompt performance across different models before production deployment
- •Security vulnerability scanning and red teaming of LLM applications to identify potential risks and compliance issues
- •Systematic comparison of model performance and cost-effectiveness to optimize AI application architecture
- •Integrating document Q&A capabilities into existing Python applications without building RAG from scratch
- •Building personal knowledge management systems that can query across multiple document formats
- •Creating AI-powered customer support tools that can answer questions from company documentation