promptfoo vs storm
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
stormopen-source
An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
Metrics
| promptfoo | storm | |
|---|---|---|
| Stars | 18.9k | 28.0k |
| Star velocity /mo | 1.7k | 30 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 0 |
| Overall score | 0.7957593044797683 | 0.3953071351250225 |
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
- +Automated multi-perspective research that synthesizes information from diverse Internet sources into structured, Wikipedia-style articles with proper citations
- +Human-AI collaborative features through Co-STORM enable interactive knowledge curation with user guidance and preferences
- +Flexible architecture supporting multiple language models, search engines, and document sources through modular components and extensive customization options
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
- -Cannot produce publication-ready articles and requires significant manual editing and fact-checking before professional use
- -Quality and accuracy depend heavily on the underlying language model and search results, potentially leading to inconsistencies or outdated information
- -Complex setup and configuration may be challenging for non-technical users despite simplified installation options
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
- •Pre-writing research assistance for Wikipedia editors and content creators who need comprehensive topic overviews before manual article development
- •Academic research synthesis for students and researchers who need to quickly gather and organize information from multiple sources on specific topics
- •Knowledge base generation for organizations that need to create structured reports from internal documents and external sources