promptfoo vs ragapp
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
ragappopen-source
The easiest way to use Agentic RAG in any enterprise
Metrics
| promptfoo | ragapp | |
|---|---|---|
| Stars | 18.9k | 4.4k |
| Star velocity /mo | 1.7k | 97.5 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 0 |
| Overall score | 0.7957593044797683 | 0.44057221240545874 |
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
- +Zero-config Docker deployment with comprehensive UI stack (admin, chat, API) included out of the box
- +Enterprise-grade architecture supporting both cloud and on-premises models with built-in vector database integration
- +Production-ready with pre-built Docker Compose templates for common scenarios like Ollama + Qdrant deployment
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
- -No built-in authentication layer - requires external API gateway or proxy for user management
- -Limited customization of UI components compared to building a custom solution
- -Authorization features are still in development for access control based on user tokens
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
- •Enterprise document search systems where teams need to query internal knowledge bases with natural language
- •Customer support automation where agents need instant access to product documentation and policies
- •Research and development environments where scientists need to search through technical papers and reports